Get the chrome extension Software Engineer - AI Systems (Go)
Stanbridge University
Stanbridge University is seeking Software Engineers – AI Systems (Go) to design, build, and operate production AI systems that perform complex, multi-stage work reliably and at scale.
This is a hands-on engineering role focused on a challenging class of problems: long-running AI workflows that call models, tools, and external APIs; maintain state across extended executions; produce structured content and generated media; interact with human reviewers; recover from partial failures; and consistently deliver accurate results to users.
The core engineering challenges extend well beyond prompt development. These systems must account for provider failures and rate limits, interrupted workflows, changing state, non-deterministic model behavior, incorrect or unsupported outputs, variable latency and cost, and deployments occurring while work is in progress.
The successful candidate will combine strong software and distributed-systems engineering judgment with practical experience building and operating LLM-backed applications, AI agents, or agent-based systems in production.
You will join at a stage where significant architecture remains to be designed and built. Engineers in this role will have substantial ownership over the patterns, services, infrastructure, and engineering standards that shape the University's AI systems.
Remote Work Eligibility
This position is eligible for remote work for candidates residing in states where Stanbridge University is currently authorized to employ remote employees. Eligible states currently include: Arizona, Colorado, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Nevada, New Jersey, North Carolina, North Dakota, Ohio, Tennessee, Texas, and Wisconsin.
Candidates must reside in an eligible state at the time of employment. Remote-work eligibility is subject to University employment requirements and may change based on applicable state requirements.
Engineering Environment
The engineering problems addressed by this team include:
- Durable workflow orchestration: Long-running, multi-stage pipelines using persistent state, job queues, checkpoints, resumability, idempotent execution, and recovery from interrupted or orphaned work.
- Multi-provider model infrastructure: Routing across commercial model providers with model registries, token and cost controls, rate-limit handling, retries, circuit breakers, health monitoring, and provider failover.
- Agent and tool orchestration: Systems in which AI agents interact with tools, APIs, data sources, application services, and deterministic business logic to complete multi-step work.
- Prompt engineering infrastructure: Treating prompts as version-controlled production artifacts with review, testing, regression protection, and measurable behavior.
- Testing non-deterministic systems: Recorded and replayable provider interactions, deterministic fixtures, evaluation harnesses, baselines, and regression testing for AI behavior.
- Correctness and quality controls: Structured-output validation, automated evaluation, domain-specific requirements, evidence checking, and safeguards against confident but incorrect model output.
- Human-in-the-loop workflows: Review and approval stages within automated processes, including systems capable of safely responding when users modify state or inputs during execution.
- Generated media pipelines: Systems capable of producing and managing documents, audio, imagery, video, and other generated assets.
- Production service architecture: Go-based APIs and backend services, relational data stores, job infrastructure, observability, and integrations supporting user-facing applications.
- Secure ingestion: Processing user-supplied and potentially untrusted documents while maintaining appropriate security and authorization boundaries.
Two Engineering Emphases
Engineers will meet the same overall technical bar but may bring deeper expertise in one of two areas:
Platform and Pipeline
Focused on distributed systems, workflow orchestration, durable state, job queues, recovery, provider infrastructure, latency, throughput, scalability, reliability, and cost optimization.
This emphasis is particularly well suited for experienced systems engineers who view AI models as powerful components that introduce a new set of distributed-systems and reliability challenges.
Agents and Quality
Focused on agent and tool architecture, prompt systems, context management, evaluation frameworks, regression testing, judge models, evidence validation, and end-to-end output quality.
This emphasis is particularly well suited for engineers who approach AI behavior through experimentation, measurement, testing, and systematic improvement.
Candidates may indicate an area of preference; however, specialization in one area is not required.
Essential Functions
- Design, develop, test, deploy, and operate production-quality software and backend services, primarily using Go (Golang).
- Architect and build AI agents and agent-based systems capable of using tools, interacting with APIs, maintaining state and context, and executing complex multi-step workflows.
- Design durable workflows that can checkpoint, resume, retry, recover, and safely continue execution following partial failures or system interruptions.
- Determine how models, agents, tools, APIs, data sources, services, and deterministic application logic should divide responsibilities within an AI system, including recognizing when an AI model is not the appropriate solution.
- Design integrations across multiple AI model providers, including routing, failover, rate-limit management, health monitoring, and degradation strategies.
- Build safeguards including structured-output validation, error handling, retries, quality gates, evidence validation, and automated recovery mechanisms.
- Develop testing strategies for non-deterministic AI behavior using techniques such as deterministic fixtures, recorded and replayed interactions, regression suites, evaluation harnesses, and quality baselines.
- Develop observability capabilities including tracing, metrics, logging, evaluation data, and replayable execution histories to support production debugging and performance analysis.
- Design human-in-the-loop workflows incorporating review, approval, intervention, and modification of workflow state.
- Build and maintain APIs, backend services, relational data models, job-processing infrastructure, and supporting application components.
- Develop secure methods for ingesting and processing user-supplied documents and other external content.
- Optimize systems for reliability, latency, throughput, scalability, output quality, and cost per execution.
- Build reusable engineering patterns and shared components that simplify the addition of new agents, model providers, tools, workflows, and output types.
- Own technical problems from initial investigation and architecture through implementation, deployment, monitoring, troubleshooting, and ongoing production operation.
- Collaborate directly with product stakeholders and domain experts to translate qualitative requirements into measurable system behavior and technical solutions.
- Contribute to architectural decisions and engineering standards for AI-powered applications across the University.
Qualifications
Required
- Substantial professional experience developing and operating production software, with strong experience in Go (Golang) or demonstrated depth in another backend language with the ability to become productive in Go quickly.
- Hands-on experience shipping an LLM-backed application, AI agent, or agent-based system into production and supporting it after deployment.
- Strong understanding of AI application architecture, including how models, agents, tools, APIs, data sources, services, and application logic interact.
- Demonstrated distributed-systems engineering knowledge, including concurrency, asynchronous processing, queues, idempotency, retries, partial failure, state management, and recovery.
- Experience designing systems that remain reliable when individual services, providers, or workflow stages fail.
- Demonstrated testing discipline for systems involving non-deterministic behavior.
- Strong experience designing and consuming HTTP APIs.
- Experience with relational databases, SQL, and persistent application state.
- Experience building, deploying, monitoring, and troubleshooting backend services in production environments.
- Understanding of software architecture, testing, debugging, observability, and production engineering practices.
- Ability to independently own ambiguous technical problems from investigation through production implementation.
- Strong analytical judgment and the ability to balance reliability, quality, performance, complexity, and cost.
Preferred Qualifications
- Experience with AI agent frameworks, orchestration patterns, or custom agent architectures, including an understanding of when a framework may not be appropriate.
- Experience with retrieval-augmented generation (RAG), embeddings, vector databases, semantic search, or knowledge-retrieval architectures.
- Experience developing evaluation and observability systems for LLM applications, including tracing, regression suites, quality dashboards, or automated evaluation.
- Experience with judge models, structured-output validation, evidence checking, or other AI quality-control mechanisms.
- Experience designing human-in-the-loop workflows involving review, approval, intervention, or modification of active workflow state.
- Experience with document processing, headless-browser rendering, text-to-speech, image generation, video generation, or other media pipelines.
- Experience with event-driven architectures, durable job queues, and asynchronous processing at scale.
- Experience with containers, cloud infrastructure, CI/CD, and production deployment environments.
- Understanding of prompt injection, authorization boundaries, data isolation, and security considerations when untrusted content is processed by AI systems.
- Experience developing systems in environments where the accuracy of generated output carries significant operational, regulatory, compliance, or safety implications.
What Success Looks Like
An exceptional engineer in this role will:
- Build AI workflows that operate reliably in production without requiring routine human intervention.
- Design systems that recover gracefully from model-provider failures, interrupted execution, deployments, rate limits, and other partial failures.
- Make AI behavior increasingly measurable, reproducible, testable, and observable rather than relying on subjective evaluation.
- Improve output quality while systematically reducing latency and cost per execution.
- Create durable engineering patterns that make subsequent agents, providers, workflows, and output types easier and safer to introduce.
- Identify when deterministic software should replace or constrain model-driven behavior.
- Build systems whose failures can be diagnosed through instrumentation and replay rather than guesswork.
- Establish architecture and engineering practices that become foundational components of the University's broader AI capabilities.
Compensation
Compensation is based on education, experience, and qualifications and internal equity
Conditions of Employment:
- A job-related assessment may be required during the interview process.
- Must be able to perform each essential duty satisfactorily and be physically present in the office (unless otherwise noted).
- Employment Authorization: Applicants must be legally authorized to work in the United States. Stanbridge University does not provide employment-based immigration sponsorship or participate in employer-sponsored or employer-dependent work authorization programs.
- Sponsorship: Sponsorship now or in the future could include having Stanbridge University sponsor, complete employer documentation, provide attestations or training plans, or otherwise participate in a work-authorization or employment-based immigration program in order for an individual to begin or continue employment. Stanbridge University does not provide any such sponsorship.
- Employment verification will be conducted to validate work experience per accreditation standards.
- Offers of employment are contingent upon the successful completion of a background check.
- Official transcripts are required prior to hire. Degrees earned outside the United States must be evaluated by a recognized credential evaluation service to determine U.S. degree equivalency and applicable subject-area coursework.
- Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.
Work Environment
- Standard professional and technology-focused work environment.
- Duties are typically performed while sitting at a desk or computer workstation.
- Position requires extensive interaction with computers, software development environments, cloud services, AI systems, and technical infrastructure.
- Subject to collaboration with cross-functional teams, changing technical requirements, and demanding project timelines.
Physical Demands
- Regularly sits for extended periods.
- Physical ability to perform department-related duties.
- Proficient in using electronic keyboards and office equipment.
- Effective verbal communication via phone, video conferencing, and in person.
- Ability to read fine print, operate computers, and understand voices clearly.
- Able to lift, carry, and/or move objects weighing 10–25 pounds as needed.
Employee Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k)
- Exciting university events
- Seasonal motivational health and wellness challenges
- Work/Life Balance initiatives
- Onsite wellness program / Staff Chiropractor
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Family Leave (Maternity, Paternity)
Institutional Values
Diversity and Inclusion
Stanbridge University’s motto, “Strength through Diversity,” reflects our deep commitment to honoring the diverse backgrounds of our students, faculty, staff, and surrounding communities. We strive to build an inclusive learning environment and uphold anti-discrimination practices in all aspects of university life.
Innovation and Technology
We embrace cutting-edge technology to enhance student learning through interactive, hands-on experiences, including virtual reality and simulation labs—ensuring students are prepared for the demands of modern healthcare practice.
Community Engagement
Through initiatives such as Stanbridge outREACH, students are empowered to give back to local and global communities, cultivating compassion, civic responsibility, and social awareness.
Equal Opportunity Employer
Stanbridge University is an Equal Opportunity Employer. We are committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity, or any other protected status. All qualified applicants are encouraged to apply.
Originally posted on Himalayas
Senior Software Engineer, AI Ecosystem
Aerospike
Aerospike is the real-time database for mission-critical use cases and workloads, including machine learning, generative, and agentic AI. Aerospike powers millions of transactions per second with millisecond latency, at a fraction of the total cost of ownership compared to other databases.
Global leaders, including Adobe, Airtel, Barclays, Criteo, DBS Bank, Experian, Grab, HDFC Bank, PayPal, Sony Interactive Entertainment, The Trade Desk, and Wayfair, rely on Aerospike for customer 360, fraud detection, real-time bidding, profile stores, recommendation engines, and other use cases.
At Aerospike, we dream big and deliver even bigger. Our mission is to unleash the power of the world’s real-time data with a database built for infinite scale, speed, and sustainability.
If you're ready to shape the future of data, join us.
At Aerospike, we built the world’s fastest, most flexible distributed database—scaling effortlessly from gigabytes to petabytes with ultra-low latency and unparalleled efficiency on modern hardware (SSDs, in-memory, and persistent memory tech).
Now, we are pushing the boundaries of real-time AI.
Aerospike is rapidly becoming the core vector and operational database engine powering next-gen AI platforms. We are looking for a sharp, autonomous software engineer to lead the charge in making Aerospike an AI-first database. In this role, you’ll own highly visible open-source projects, pioneer integrations across the AI ecosystem, and write clean, high-performance code that powers mission-critical applications globally.
What You’ll Do
- Build AI-First Tooling: Design, architect, and deliver high-impact open-source tools and integrations connecting Aerospike to modern AI/ML frameworks.
- Optimize Core Performance: Continuously refine software for extreme scalability, ultra-low latency, and production efficiency.
- Collaborate Across the Stack: Work directly with customers, product leaders, and solutions architects to translate complex real-world requirements into production-ready software.
- Own the Integration Roadmap: Partner cross-functionally across client and server teams to shape feature sets built explicitly for real-time AI workloads.
What You Bring
- 5+ years of hands-on software development experience in Go, Python, Java, Scala, C, or C++.
- Systems & Integration Expertise: Proven track record building integrations, API clients, software SDKs, or distributed systems tooling.
- Production Engineering Focus: Strong grasp of modern software architecture, container environments (Kubernetes, Docker), and building resilient, automated software.
- Self-Driven Ownership: High autonomy, strong initiative, and a passion for engineering excellence with minimal supervision.
Bonus Points
- Hands-on experience with NoSQL, vector databases, or active contributions to open-source communities.
Got code to show off? Share a link to your GitHub, StackOverflow profile, or favorite open-source contributions with your application!
Salary Range: $160,000 – $205,000
(Actual compensation will be based on experience, location, and other relevant factors.)
Aerospike is an Equal Opportunity Employer. We are committed to an environment free from discrimination based on race, religion, gender identity, sexual orientation, age, disability, or veteran status.
To keep our recruitment pipeline as high-performance as our database, Aerospike uses AI-driven tools to streamline resume reviews, scheduling, and communication. While we love smart tech, we value human connection even more. AI helps us optimize the process, but real people make 100% of the actual hiring decisions.
Originally posted on Himalayas
Sr Staff Software Engineer ( AI Agents, Observability)
ServiceNow
Job Description
Come join our team. We're rapidly expanding AI agents for Observability group and building a new generation of AI Agents designed to transform how enterprises investigate, respond to, and remediate critical incidents and alerts. Our mission is to reduce mean time to resolution (MTTR) by orders of magnitude, turning reactive firefighting into proactive, intelligent automation.
The AI agents for Observability combines the intelligence of large language models, the predictive power of observability analytics, and the judgment of human incident commanders, all within a single governed process that runs on ServiceNow's enterprise-grade platform. This dramatically reduces the time and expertise required to triage a multi-service incident and enables organizations to maintain SLOs even as their infrastructure complexity grows.
What you get to do in this role:
- Design and architect the next generation of intelligent incident and alert investigation and remediation systems, enabling enterprises to automate away toil and focus on innovation.
- Drive the technical direction for assigned projects,defining architecture, design patterns, best practices and leveraging the latest AI/ML models for integrations.
- Collaborate with product managers, other software engineers, UX Designers and security experts to build product features that meet business objectives and security standards.
- Lead architecture reviews and technical decision-making on high-impact initiatives spanning multiple teams.
- Create tools, libraries, and frameworks for other developers to build upon
- Stay current with industry trends and emerging technologies and leverage them to drive innovation and improve our platform.
- Mentor and influence engineering excellence across the incident intelligence domain, helping colleagues apply emerging agentic patterns responsibly.
- 12+ years of experience building scalable software applications using Java or a similar object-oriented programming language
- Strong experience with JavaScript or TypeScript and modern frontend engineering practices
- Strong foundation in data structures, algorithms, object-oriented design, and performance optimization
- Proven expertise in multi-agent systems design: orchestration frameworks, agent communication patterns, state management across distributed agents.
- Practical experience integrating foundation models and LLMs into production systems, with awareness of hallucination, latency, cost, and safety trade-offs.
- Proven ability to solve significant technical problems with broad impact across teams or product areas
- Demonstrated expertise in architecture, technical strategy, and driving complex initiatives from design through delivery
- Experience mentoring engineers and influencing engineering excellence through reviews, standards, and technical leadership
- Practical experience using AI-assisted coding tools to improve development workflows and engineering productivity
- Strong analytical and software design skills
For positions in this location, we offer a base pay of $190,900 - $334,100, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
Work Personas
We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.
Equal Opportunity Employer
ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.
Accommodations
We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact for assistance.
Export Control Regulations
For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.
From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.
Join us to put AI to work for people.
Originally posted on Himalayas
Software Engineer, AI Platforms
Figma
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us!
AI Platform teams build the core frameworks, abstractions, and systems that support AI features across Figma. We create new capabilities that AI product teams can build on, while collaborating with teams from around the company to improve our performance, reliability, and technical quality. We’re looking for strong infrastructure and platform-minded engineers to contribute to our agent infrastructure, context retrieval & ranking platform, and core AI services, in order to accelerate our most critical company-wide AI initiatives.
Here are just a few areas our platform teams work on:
- Evals for design: Building evaluation frameworks for design generation quality that are used across every Figma AI feature.
- Agentic search: Providing relevant context from throughout the Figma ecosystem to agents via search tools, in order to improve agent quality.
- Figma MCP: Making our MCP server faster, more reliable, and easier for internal & external engineers alike to develop on.
- Agent infrastructure: Iterating on the sandboxes, harnesses, and tools leveraged by the agents that power Figma Make and the Figma Design Agent.
- Preview & publishing platforms: Creating the shared platforms for building, previewing, and publishing code written by agents via Figma Make.
This is a full time role that can be held from one of our US hubs or remotely in the United States.
What you’ll do at Figma:
- Support end-to-end AI feature development by designing, building, and maintaining systems that are scalable, reliable, and performant.
- Collaborate with product, research, and infrastructure engineers working across AI.
- Help shape our platform strategy, roadmaps, and engineering best practices throughout the organization.
- Debug and resolve complex production issues that span services and layers of the stack.
- Mentor engineers and foster a culture of collaboration, inclusivity, and technical excellence.
We'd love to hear from you if you have:
- 5+ years of Software Engineering experience, especially in backend or infrastructure engineering.
- 3+ years of experience in one or more of: AI, search infrastructure, or developer platforms.
- Proficiency in TypeScript, Python, or Ruby.
- Strong system design skills and a track record of architecting resilient production systems.
- Excellent communication skills, with experience collaborating across teams and mentoring others.
While not required, It’s an added plus if you also have:
- Prior experience in building AI infrastructure at scale, whether that be model training or serving platforms.
- Experience evolving large-scale, shared developer platforms to improve reliability and developer velocity.
- A proven track record of leading technical strategy and complex, multi-team projects.
At Figma, one of our values is Grow As You Go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Pay Transparency Disclosure
Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location.
Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law.
At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities.
Examples of accommodations include but are not limited to:
- Holding interviews in an accessible location
- Enabling closed captioning on video conferencing
- Ensuring all written communication be compatible with screen readers
- Changing the mode or format of interviews
To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding.
By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice.
Originally posted on Himalayas