Founding Applied AI Engineer
About the role
Who Are We? A stealth, seed-stage AI infrastructure company is building the next generation of computer-use agents for enterprise workflow automation, beginning with one of the most complex and high-impact sectors in the economy: healthcare. The company’s platform enables AI agents to interact with real enterprise software systems, including electronic health record environments, and execute high-value workflows such as appointment scheduling, orders, data extraction, and operational automation. This is not a research lab producing isolated model demos. The team is building production-grade AI systems that must operate reliably in high-stakes, regulated environments. The work sits at the frontier of multimodal perception, model grounding, agent recovery, evaluation systems, inference optimization, and production deployment. The company is led by an experienced, second-time founding team with prior successful exits and deep technical pedigree. The broader team includes builders from elite technology companies and top academic institutions, with backing from a premier venture firm and $10M in seed funding. Early traction already includes meaningful ARR and enterprise healthcare customers, creating a rare opportunity to join at the founding stage while working on a product with immediate real-world demand. What’s in It for You? Founding-level ownership: Join as one of the earliest applied AI engineers and directly shape the architecture, product direction, and technical culture from the ground up. Frontier AI, shipped to production: Work on agentic systems that actually operate real enterprise software, rather than prototypes that never leave the lab. Massive equity upside: Enter at the seed stage of a well-capitalized company with early revenue traction, enterprise demand, and a mission tied to national-scale healthcare efficiency. Elite technical environment: Collaborate with experienced operators and engineers from billion-dollar technology companies, top research institutions, and prior venture-backed exits. Greenfield infrastructure: Build core systems from scratch across agent reliability, multimodal perception, evaluations, synthetic data, inference, and backend automation. High-impact domain: Your work will directly improve workflows in healthcare and other regulated sectors where reliability, safety, and operational efficiency matter deeply. Career-defining scope: This role offers the chance to grow into a long-term technical leader as the engineering organization scales. What Will You Do? Applied AI & Agent Systems Build the AI systems powering enterprise-facing API products, with a focus on computer-use agents that can operate complex software environments safely and reliably. Develop agentic workflows for real-world healthcare operations, including appointment scheduling, order handling, data extraction, and administrative automation. Improve multimodal perception, grounding, recovery, and reasoning loops for agents interacting with dynamic enterprise software interfaces. Reliability, Evaluation & Model Quality Design evaluation systems that measure agent reliability, detect regressions, and surface failure modes before they reach production. Conduct failure analysis across model behavior, tool use, perception errors, hallucination risk, and workflow breakdowns. Build recovery loops that allow agents to handle ambiguity, errors, incomplete state, and unexpected software behavior. Create synthetic data pipelines and tooling to improve model quality, agent robustness, and production reliability. Production Engineering & Infrastructure Productionize model-powered systems using clean, reliable Python and strong backend engineering practices. Optimize inference performance, deployment patterns, and routing across frontier models and smaller fine-tuned models. Contribute to backend infrastructure and product features that combine AI, automation, workflow orchestration, and enterprise integrations. Translate ambiguous product and customer needs into well-designed systems, high-quality code, and measurable product outcomes. What Will You Need? Strong experience in applied ML, AI engineering, or production-oriented machine learning systems. Hands-on experience with one or more of the following: LLMs, multimodal systems, vision-language models, autonomous agents, computer-use agents, perception systems, or model-powered products. Strong production Python skills and the ability to ship reliable, maintainable systems beyond research prototypes. Experience with model evaluation, prompt/system design, error analysis, model iteration, regression testing, and production observability. Comfort working across the stack when needed, especially where AI systems meet backend infrastructure, APIs, product workflows, and deployment. Strong computer science fundamentals, including data structures, algorithms, runtime/space complexity, memory concepts, and system design tradeoffs. Product intuition and the ability to turn ambiguous customer problems into shippable technical solutions. Startup mindset: high ownership, low ego, fast iteration, comfort with ambiguity, and excitement about inventing infrastructure from scratch. Bonus: ML research experience, especially involving agents, multimodal models, VLMs, RL, inference optimization, or synthetic data generation. Bonus: Experience building systems for healthcare, regulated industries, enterprise automation, or high-reliability workflow software.
What we are looking for
- Bachelor's degree from Harvard, Princeton, Stanford, MIT, CalTech, or CMU
- Bachelor's Degree in Computer Science or an adjacent (Computer Engineering, Software Engineering, ML/DS, Information Science, EE, Math, Biology)
- 1-5 years of experience
- Owned & shipped features/projects end to end
- Robotic Process Automation (RPA) experience
- Computer vision background (NLP/LLM ok too)
- High School math competition or a strong academic record
- Research publications or notable ML projects