What You'll Do
Own the agent architecture. Decide the loop, the tools, the context strategy and the evaluation harness for our claim-audit agents. Choose between single-agent and multi-agent topologies, between LLM reasoning and deterministic post-passes, between retrieval and direct context loading — and defend each choice with data.
Set the evaluation strategy. Define, with our clinical and coding experts, what expert-level accuracy means in measurable terms. Build the eval infrastructure the team ships against, diagnose where systems fail, fix the root cause, and prove the fix moved the metric.
Engineer context at scale. Long, noisy medical records; citation-grounded findings; structured outputs and tool surfaces that models call correctly every time.
Lead through ambiguity. Turn open-ended problems into technical plans, lead projects that span several engineers and quarters, and make the calls on model choice, build versus buy, and the trade-offs between cost, latency and accuracy.
Run it in production. Own reliability, observability and cost control for LLM workloads that run on real claims every day.
Raise the bar. Mentor engineers, review designs and code, and set the team's standards for agent design, evals and engineering quality — including how we use Claude Code, Codex and similar tools to move faster than most teams can without them.
Become a domain expert. Healthcare claims, coding guidelines, and the medical record itself are unavoidable parts of the job. Strong engineers who lean into the domain become outsized contributors here.
What We're Looking For
Required
8+ years of software or ML engineering experience, including 3+ years building applied ML / AI systems in production, with a Bachelor's in CS, Math, Engineering or a related quantitative field (Master's or PhD preferred).
Staff-level technical leadership: you've set the architecture of a production system other engineers built on, and led work across a team.
Hands-on generative AI experience: training or fine-tuning generative models, or building agentic systems that solve real business problems in production.
Deep, hands-on understanding of agent loops — how a model decides to call a tool, how a tool result re-enters context, how loops terminate, where they fail.
Hands-on experience with at least one major agent SDK — OpenAI Agents SDK, Anthropic SDK / claude-agent-sdk, LangGraph, or equivalent — and a well-argued view of the trade-offs.
Working knowledge of how modern coding agents are built and how they engineer context, and fluency with Claude Code / Codex as a power user — including reading their source when needed to understand or extend behavior.
Strong Python engineering that sets the standard for a team: clean abstractions, type discipline, async, tested code.
Solid command of VS Code and git — branches, rebases, worktrees, conflict resolution, PR workflows.
A bias toward measurement: you've built evals a team relies on, you don't ship without one, and you don't believe a number you can't reproduce.
Strongly preferred
Prior work on long-context, citation-grounded systems where the model must point to evidence, not just answer.
Production experience with caching, observability, and cost and latency control on LLM workloads at scale.
Experience designing structured outputs (Pydantic / JSON Schema) and tool interfaces that LLMs reliably call correctly.
Familiarity with reasoning models (o-series, Claude extended thinking, Gemini thinking) and a sense of when they earn their cost.
Healthcare, legal, finance, or any other domain where "mostly right" is unacceptable.
Nice to have
Document understanding (OCR, layout-aware models, table extraction).
Vision-language models, multimodal retrieval.
Published work or substantial open-source contributions on agents, evals or LLM systems.
What We Offer
Work from anywhere in the US! Machinify is digital-first.
Top Medical/Dental/Vision offerings
FSA/HSA
Tuition reimbursement
Competitive salary, 401(k) with company match
Unlimited PTO
Additional health and wellness benefits and perks
Flexible and trusting environment where you'll feel empowered to do your best work
The salary for this position is based on an array of factors unique to each candidate, such as years and depth of experience, skill set, and certifications. The base salary range for this role is [L6 range: confirm with Comp before posting]. Compensation also includes meaningful equity, healthcare, unlimited PTO, and more.
Equal Employment Opportunity at Machinify
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace. Machinify is an employment at will employer. We participate in E-Verify as required by applicable law. In accordance with applicable state laws, we do not inquire about salary history during the recruitment process. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiters know. See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/
