Thesis Care is an AI-powered care team platform that combines AI agents with expert clinicians to deliver scalable clinical capacity.
<p><strong>About us</strong></p> <p>Thesis is building the AI-powered care team platform for infinitely scalable clinical capacity. We radically increase access and improve quality of care by combining AI agents with clinical experts to take on high-impact clinical operations and care management activities for healthcare organizations.</p> <p>We’re based in NYC, are growing rapidly, and are backed by $60 million in funding from Oak HC/FT, CRV, Black Opal Ventures, and experienced C-level healthtech angel investors.</p> <p><strong>About the role</strong></p> <p>We are looking for a highly capable Product Engineer to help build and scale Thesis’s AI Care Team platform. This is a broad-scope, hands-on engineering role for a technical builder with strong ML fundamentals, production experience, and a product-oriented mindset. As an early member of the engineering team, you will play a critical role in designing, training, deploying, and operating the ML systems that power Thesis’s AI coordinator.</p> <p><strong>Responsibilities:</strong></p> <ul> <li><strong>Build and own AI systems end-to-end:</strong> Ingest, structure, and analyze large volumes of unstructured healthcare data, and design production-grade data and ML pipelines for both training and inference.</li> <li><strong>Develop NLP and LLM-powered features:</strong> Design, evaluate, and deploy models using modern NLP frameworks and LLM APIs, with a strong focus on real-world performance and reliability.</li> <li><strong>Operate at production scale:</strong> Architect and maintain cloud-based ML workflows in AWS, including containerized services and orchestration for data processing, training, and inference.</li> <li><strong>Continuously improve model quality:</strong> Monitor, test, and iterate on model accuracy, robustness, privacy, and safety within live clinical workflows.</li> <li><strong>Contribute across the product:</strong> Collaborate across the stack—from APIs and backend systems to product UX and workflow design—to deliver cohesive, AI-driven features.</li> </ul> <p><strong>We expect you to have:</strong></p> <ul> <li><strong>ML and data depth:</strong> 3+ years of experience ingesting, structuring, and analyzing diverse data sources, with strong proficiency in Python, SQL, and data tooling (e.g., pandas or equivalent).</li> <li><strong>Production AI experience:</strong> Hands-on experience building and operating NLP and/or LLM-powered systems in production, including frameworks such as spaCy, LangChain, and extensive LLM API usage.</li> <li><strong>Pipeline expertise:</strong> Significant experience designing and maintaining data and ML pipelines in production environments.</li> <li><strong>Cloud and infrastructure fluency:</strong> Experience working in AWS environments, including containerized workloads and orchestration for training and inference.</li> <li><strong>Healthcare familiarity:</strong> Experience working with healthcare data and an understanding of the constraints of regulated environments, or strong motivation to develop this expertise.</li> <li><strong>Cross-functional communication:</strong> Ability to collaborate effectively with engineers, product leaders, and clinical stakeholders.</li> <li><strong>Ownership mentality:</strong> Demonstrated success operating in early-stage or high-growth environments with broad technical responsibility.</li> <li><strong>Full-stack capabilities:</strong> Comfort contributing beyond core ML work, including APIs, system design, or frontend-adjacent development when needed.</li> <li><strong>NYC-based</strong>: You are based in New York and excited to be in-office ~3 days per week.</li> </ul> <p>Target compensation for this role is $200-$250k, plus equity and a generous benefits package.</p> <p>Thesis Care is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.</p>