SaaS Tech Sales CV & LinkedIn for Non-Native – AE/Senior BDR Transition (Remote EU/US)
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Warmed up Linkedin accounts needed
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Sr Manager, Talent Acquisition
Program Manager, Growth
Account Executive, SMB - LinkedIn Talent Solutions
Software Engineering Manager, Systems Infrastructure, Development Infra
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
This role will be hybrid in LinkedIn's Bellevue office.
We are a data driven organization and you will lead strategic investments to make step function improvements in the lives of LinkedIn developers. This spans multiple areas including but not limited to: Providing tools and infrastructure that creates delightful development experience; Provide and support application frameworks that empower LinkedIn Engineers to build member facing products; Provide actionable insights that leverages data and metrics; Provide tools and data that help LinkedIn teams listen to their customers; and most importantly deliver reliable and scalable infrastructure. You will have the opportunity to engage the industry-wide developer community and contribute to open-source software as well. This Software Engineering Manager role is part of the Quality Platforms team within DPX. You will get an opportunity to influence, transform, and create an amazing experience for developers at LinkedIn by leading the team and driving Quality charter to build testing tooling and infrastructure that serves the developer community within LinkedIn. The mission is to create an ecosystem that enables teams to deliver products and services at LinkedIn at the highest quality they can.
Responsibilities
You will build and ship software at scale that delivers impact.
You will improve all aspects of developer experience with a data driven mindset.
You will design and build tools and frameworks to automate development, testing, deployment, management, monitoring, data gathering and analysis of our 24x7 services and products.
You will provide thought leadership, develop and evangelize solutions to challenges faced by every product and infrastructure team at LinkedIn to improve developer happiness, productivity, and efficiency.
Design products/services/tools and code that can be used by others while upholding operational impact of all decisions
You will scale the infrastructure and tools required to keep our 6000+ developers in step when they are all sharing the same code, building and testing our software stacks, and releasing and deploying their services continuously without compromising site reliability.
Work closely with and influence product and/or technology partners regularly to help define roadmap
You will review others' work, provide architectural guidance and mentorship to up-level the engineering organization, resolve conflicts between teams within the organization to get alignment and build team culture.
You will identify problems and opportunities and provide technical leadership, defining and undertaking best engineering practices to initiate, plan, and execute critical, large-scale, cross-functional, and company-wide programs.
Sr. Software Engineer, Systems Infrastructure
This role will be available in Bellevue, Washington.
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
We're building the next-generation data infrastructure, including storage, streams, media and analytics platforms. Help us scale LinkedIn infrastructure to handle massive data growth across the LinkedIn ecosystem as we experience dramatic growth in membership and products. Bring your experience in distributed systems and algorithms and a strong systems orientation (multi-threading, concurrency, scalability, performance). You should understand frameworks for caching, queuing, and distributed data storage, and be excited to work on cutting edge open-source systems.
What You’ll Do:
The ideal candidate will help scale LinkedIn’s infrastructure to handle massive growth in membership, traffic, and data as we continue to experience dramatic growth in the usage of our products with focus in one or more of the areas below:
- Data Infrastructure: A focus on building and supporting large scale systems and tools that enable the generation of insights and data products on all of LinkedIn’s internal and external data via self-serve computing, reporting solutions, and interactive querying.
- Search, Networks and Analytics: Build and operate the platform that powers all of search at LinkedIn—responding to thousands of queries per second with target latencies in tens of milliseconds. The goal is to provide and run in 24/7 production environment a platform that enables search quality engineers to rapidly innovate, experiment and improve relevance—while at the same time remaining constantly available and performant to our users.
- Service: Provide the technical platform for all of LinkedIn Engineering to build services, which are the essential unit of development and deployment.
- Content and Community: Deliver the systems and algorithms that generate and serve feeds of professionally relevant activities and content.
AI Architect - Business Applications
This role will be based in San Francisco, CA, NYC, NY, Bellevue, WA or Chicago, IL
At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together.
The Tech & Analytics team builds the analytical and automation foundation that powers LinkedIn's most important Go-to-Market decisions. We partner across Sales, Customer Success, Marketing, and Engineering to create a unified understanding of GTM performance. Our mission is to transform data into proactive insights and intelligent systems that guide LinkedIn's growth and efficiency.
We're hiring an AI Architect to design, build, and launch AI-powered features inside the business applications our employees use every day. You'll work as a product-minded software builder — turning LLMs, agents, and AI-driven automation into reliable, well-scoped features that meaningfully improve how our internal teams get work done.
This is a hands-on builder role. You'll own features end-to-end: from problem framing and prototyping, through evaluation and rollout, to monitoring in production. You'll partner closely with product managers, data engineers, and platform teams to ship AI capabilities that are ready to support an enterprise sales organization.
Responsibilities:
- Define the technical roadmap and architecture for Technology & Product Operations org, including key decisions on frameworks, tooling, and practices. Partner with R&D to build applications leveraging our internal platforms, as well as provide input to R&D on enhancements to our technical platforms and data infrastructure
- Lead the hands-on design, development, and deployment of scalable data products, AI/ML models (e.g., member friction, customer impact, anomaly detection), and GenAI-powered agentic workflows.
- Serve as the subject matter expert on applying modern AI, LLMs, and ML techniques (e.g., RAG, fine-tuning) to solve GTM business problems within Enterprise Applications in partnership with Operations, Data Science and Engineering team
- Design for quality and trust: define evaluation criteria, build active monitoring, implement safe use guardrails, and continuously measure AI feature performance against business outcomes.
- Mentor operations and analytics colleagues on AI tooling and applications, setting a high bar for technical rigor, code quality, and engineering best practices through a lead-by-example approach.
- Operate at scale and in production: instrument features to minimize latency and cost, maximize reliability and accuracy; debug failure modes; iterate based on real usage.
- Collaborate with Product, Engineering, and Data Science teams to operationalize and scale models from prototype to production, ensuring reliability and measurable business impact.
- Translate complex technical concepts and model outputs into clear, concise, and actionable narratives for non-technical stakeholders and senior leadership.