Staff Software / AI Engineer

Job description

About the role Pathos is building the first AI native biotech platform, one that turns massive multimodal datasets into foundation models, and those models into live agents that materially change how drug development is done. Instead of bolting AI onto legacy pharma systems, we are designing a new stack from the ground up. We are hiring an engineer to architect and ship the first generation of Pathos AI and data infrastructure. Your work will power our core products and run the agents that support BD, clinical development, computational biology, and lab teams. This is an early role with broad technical scope and full ownership. If you want to build the technical infrastructure that rewires the R&D workflow, this is the place to do your career's best work. It is one of the most direct applications of your engineering skill to advances in cancer research. What You’ll Build Build AI agents and copilots for internal teams across BD, clinical, computational biology, and lab. Build robust data pipelines into a governed warehouse and knowledge graph. Build MCP style servers and tooling so agents can safely talk to internal systems. Work across the full product lifecycle: prototype, iterate, ship, and maintain. Who You Are You have architected and owned a production system end to end, from design through observability, that real users depend on. You have shipped LLM powered workflows in production, not just prototyped them. You know how technologies like LangGraph work and not just use it as a library. You make high judgment technical calls at the system level, not just execute well-scoped tasks. You move quickly, take initiative, and operate with a strong sense of ownership. You like working closely with end users and shaping products from zero to one. Must-haves: Roughly 8 or more years of professional software, ML, or product engineering experience building systems that real users depend on. We read this as a signal of demonstrated scope, not a hard gate. Fluent in Python and TypeScript in production, and comfortable building full stack applications. Experience with production grade APIs, eventing, and data engineering, ideally cloud native on GCP, BigQuery, or similar. You own services end to end: architecture, implementation, testing, and observability. Bonus: Prior work on agentic systems at scale, retrieval systems, or ML infrastructure. Experience operating in highly regulated environments that use patient and clinical trial data. Location This is a hybrid role, requiring 4 days per week onsite, in our NYC Headquarters. Drug development shouldn’t be guesswork, not when patients are waiting. Pathos is building a next-generation biotech with AI at the core. Not as a feature, but as the operating system for how medicines get developed. We believe most drugs don’t fail because the science was wrong. They fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn’t answer the real question: who benefits, and why? Pathos exists to change that. We’re building the largest foundation model in oncology and pairing it with proprietary AI systems, deep oncology expertise, and 200+ petabytes of multimodal data linked to patient outcomes, so we can make development decisions with more precision, much earlier. This is not theoretical. We’re well-capitalized and have the leadership to build a generational company. We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy. So therapies reach the patients most likely to benefit, sooner. How We Build Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2–4 subject-matter experts who each command hundreds of AI agents to do the work that used to require dozens of people. Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today. The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower.