AI Jobs

About Praxent Praxent builds software for financial services companies — banks, credit unions, lenders, insurers, and wealth platforms. We have been doing it for over twenty years, from Austin, with a delivery team spread across the US and Latin America. We are an Anthropic partner, and building agentic AI systems inside our clients' environments is now a core part of the work rather than an experiment on the side. This role exists because that work needs a different kind of engineer than a traditional project team supplies — someone who sits with the client, decides what should get built, and builds it. This is the first Forward Deployed Engineer hire at Praxent. You would be defining the role, not inheriting it. This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice which must be identified to the Company. Employees may live in the following locations: Texas, Colorado, Florida, Georgia, Massachusetts, Maryland, Minnesota, North Carolina, Nebraska, Oregon, Pennsylvania, Tennessee, South Carolina, Washington. Applicants for this position must be currently and legally authorized to work in the United States without the need for current or future sponsorship (e.g., H-1B, J-1, F-1, CPT, OPT, etc.). Praxent will not offer immigration sponsorship or assume sponsorship of an employment visa for this position. International relocation or remote work arrangements outside of the U.S. will not be considered. About the role A Forward Deployed Engineer at Praxent embeds with a client, figures out what they actually need, and builds it. Not a spec handed down from a product manager — you are in the discovery conversation, you decide what gets built, and you ship it to production and stay until it works. You will spend more than half your time in front of the people whose problem you are solving, and the rest of it writing the code that solves it. You are not shielded from the client. You are not handed a ticket queue. When a client asks for the wrong thing, talking them into the right thing is your job, not someone else's. The shorthand we use internally is founder-level autonomy with staff-engineer-level rigor . You make the technical call in the room and defend it, rather than routing it back for sign-off. That is a real grant of authority, and it comes with real accountability for the outcome. Financial services makes this harder and more interesting than it would be elsewhere. The systems are old, the data is regulated, the stakeholders are risk-averse for good reasons, and the gap between a working demo and something a bank will actually run in production is where most AI projects die. Closing that gap is the whole job. What you'll do Own technical delivery end to end on your accounts — discovery, architecture, build, production, and the messy weeks after launch. Embed with client engineering and business teams. Translate a VP's business problem into an architecture, and that architecture back into language the VP can defend to their board. Build production applications and agentic systems inside client environments: LLM-backed workflows, agent orchestration, evaluation frameworks, and integrations against core banking, lending, policy administration, and financial data systems. Design the integration surface. Most of this work is API design and systems integration against platforms that were not built to be integrated with. Harden prototypes into systems a regulated institution will actually run — security review, audit trail, PII handling, performance under real load. The distance between a working demo and a production deployment at a bank is most of the job. Scope down. Identify when a client is asking for the wrong thing and talk them into the right thing. This is the part of the job we care most about. Guide the client's own engineers. Part of leaving well is that their team can extend what you built without calling us. Own the hardest production problems on your accounts — the ones that surface at 4pm on a Friday in someone else's infrastructure. Own the implementation plan and direct the work. You decide the sequence, and as an engagement scales you delegate to the engineers around you — including the internal AI engineers who will work for you. This is a technical leadership role, not a people-management one. Make the case for growth by building things worth expanding. You carry no revenue target and you do not run the commercial conversation — our Client Partners do that. Your argument is the solution itself. Flag risk before it becomes a problem. Leave behind documented, runnable, maintainable software that still works after you rotate off. What we're looking for 6+ years shipping production software, full-stack. Python is what most of this work is written in; .NET or Node are fine substitutes. React or Next.js on the front end. Experience leading the technical side of a delivery. You have owned an implementation plan, sequenced the work, and directed other engineers without being their manager. API design and systems integration. You have integrated against something old, poorly documented, and load-bearing, and made it hold. Real client-facing delivery experience. You have run a discovery session, handled a difficult stakeholder, and delivered an account without someone above you managing the relationship. Production experience with LLMs and agents. Prompt engineering, agent development, evaluation frameworks, deployment at scale. Application engineering, not ML research. Comfort with ambiguity. You deliver value before all the requirements are defined, because they never are. Range in the room. You are precise with engineers and clear with executives, and you can switch between those two rooms inside an hour. A story about scoping down. A time you told a client not to build something, and why you were right. Willingness to travel to client sites 20–30% of the time. What You’ll Love About Us: Stability . We've been in business for over 25 years. Work stays at work . We promote a healthy work/life balance to help ensure you have the time that you need. We encourage no more than a 40 hour work week. Great company culture . We’ve been recognized by Texas Monthly, Clutch, Comparably, and more for the quality of our workplace. Feel free to check out our rating on Glassdoor . We’re here to empower you . It’s your work and your career. Our management team is here to help you become who you want to be. Not to micromanage you. Stay healthy . We offer medical, dental, and vision coverage as well as wellness days. We also provide disability insurance and we even have a wellness program. Plan for the future . We don’t want you to work here forever. Save for retirement with an IRA and we’ll match up to 3% every year. We value your ideas . At Praxent, our doors are always open. Need help? Come on in. Have a vision for the future of the company? We’d love to hear it. Rest and relaxation . Employees enjoy 15 days of PTO, 9 US holidays, 5 sick days, and a closed office the last week of the year. Employees earn more PTO each year. You’re more than an employee, you’re a person . Every co-worker you’ll meet is committed to treating you with respect and kindness. You won’t hear stuff like, “It’s just business.” Family values . Praxent provides paid parental leave. The US base salary range for this full-time position is $205,000 - $250,000 + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. #LI-Remote

Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), we develop autonomous-science capabilities for cellular and tissue biology, spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data that integrate deep biological expertise with foundation modeling and agentic systems. We are seeking a Senior Machine Learning Scientist to help execute this vision by building autonomous life science systems grounded in epistemology, scientific methodology, Bayesian argumentation, and automation. This role will translate the scientific direction of Autonomous Life Science AI into working architectures, workflows, and evaluation methods that allow AI systems to reason rigorously about biological hypotheses, propose experiments, incorporate evidence, and accelerate discovery. This is a hands-on scientific and technical role for someone who can operate at the intersection of machine learning, biological reasoning, agentic systems, and experimental design. The right person will be comfortable formalizing how scientific knowledge is represented, how uncertainty is handled, how evidence changes belief, and how automated systems can execute increasingly rigorous cycles of life science discovery. What You'll Be Building Build autonomous life science systems that connect AI reasoning, biological evidence, experimental design, and automated execution. Translate the broader Autonomous Life Science AI vision into concrete architectures, workflows, prototypes, and production-quality research systems. Develop methods for representing hypotheses, uncertainty, evidence, and scientific arguments in ways that enable robust machine reasoning. Apply Bayesian reasoning, epistemology, and scientific methodology to the design of AI systems that can propose, test, and revise biological hypotheses. Design agentic workflows that plan experiments, reason over results, and close the loop between computational predictions and automated laboratory feedback. Partner with ML scientists, experimental scientists, automation teams, and platform teams to ensure systems are biologically grounded and experimentally actionable. Build evaluation frameworks for autonomous discovery systems, including benchmarks for reasoning quality, hypothesis generation, evidence integration, and experimental utility. Contribute to the technical roadmap for autonomous life science research systems and help raise the scientific rigor of the team’s approach. What You'll Need to Succeed PhD in Computer Science, Machine Learning, Computational Biology, Statistics, Biology, or a related quantitative field. Strong research track record in machine learning, AI for science, computational biology, probabilistic modeling, agentic systems, or a related area. Deep understanding of scientific reasoning, experimental design, uncertainty, and evidence integration. Experience building or researching systems that reason over complex scientific, biological, or experimental data. Strong foundation in modern ML methods, with hands-on experience in frameworks such as PyTorch, JAX, or TensorFlow. Ability to translate biological questions into computational and ML problems, and to translate ML system behavior back into scientific terms. Strong technical judgment, with the ability to operate in open-ended research settings where the right architecture, abstraction, or evaluation method is not yet obvious. Excellent collaboration skills across AI, biology, automation, and platform teams. Bonus Points For Experience with Bayesian modeling, probabilistic programming, causal inference, or formal methods for reasoning under uncertainty. Experience building agentic, active-learning, closed-loop, or autonomous-science systems. Familiarity with biological data modalities such as single-cell omics, perturbation data, imaging, spatial profiling, genetics, or multi-omics. Experience designing systems that generate, rank, test, or revise scientific hypotheses. Background in philosophy of science, epistemology, scientific methodology, or formal argumentation. Experience integrating computational predictions with experimental or automated lab workflows. Track record of publishing or presenting work at premier ML, computational biology, or scientific venues. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $268,000 — $336,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.