Senior Applied AI Engineer

Key Responsibilities

• Build and ship internal AI tools and features end to end — from a rough idea to a deployed and maintained product.
• Set the technical direction and reusable patterns the AI Engineering team builds on and raise the engineering bar across the company by example.
• Design and deploy LLM and agentic systems — retrieval, tool use, orchestration, and evaluation — with sensible guardrails and humans in charge where it matters.
• Partner with engineering, hardware, operations, and other teams to turn their ideas into working tools they can own.
• Create shared components, templates, and infrastructure that let other teams build faster.
• Right-size models and optimize for cost, latency, and reliability; instrument usage and quality.
• Mentor teammates and help grow the group’s talent fast; model strong engineering practice — clear specs, real documentation, tests, and honest estimates.
• Drive work to completion independently — and, when needed, make the call to stop the work early and cleanly.

Required Qualifications

• Deep software-engineering expertise and a strong track record of shipping and operating production software at scale (primarily Python), with excellent testing, code review, CI/CD, and documentation habits.
• Substantial hands-on experience building LLM-powered applications: prompt and agent design, tool/function calling and MCP-based integrations, retrieval-augmented generation, and rigorous evaluation.
• Proven work with agentic systems and agentic coding workflows — designing or integrating agents that take actions against real systems safely.
• Architectural judgment: able to design systems others build on, and deliver end to end across backend services, APIs, light front-end, and deployment.
• High autonomy and ownership — thrives in ambiguity and reliably carries a project from idea to production with little oversight.
• Demonstrated record of impact you can point to. Publications and notable open-source or research contributions count, but we weigh shipped software and fast, real-world delivery over research output.
• A force multiplier for those around you: clear communicator, natural mentor, and able to translate a non-expert’s need into a working tool.

Preferred Qualifications

• LLMOps / MLOps: model serving, monitoring, evals, and cost/latency optimization (right-sizing models).
• Data and ML engineering: pipelines, embeddings, and vector databases; fine-tuning or adapting models.
• Deep-learning frameworks (e.g., PyTorch) and experience building or adapting generative models such as diffusion or image generation — useful if the team takes on more model-building work over time.
• Front-end / UX for internal tools (e.g., React / TypeScript).
• Experience with modern coding agents and the broader agent-tooling ecosystem.
• Kubernetes, Docker, and CI/CD tooling.
• Exposure to scientific, hardware, lab-automation, or other complex operational environments.

What Success Looks Like

• You ship useful tools into production quickly, and teams actually adopt them.
• Your work is well-documented, tested, and dependable.
• You operate independently and reliably move projects to done — or to a clean stop.

Pay

The approximate base salary range for this position is $175,000 - $210,000. We consistently monitor external market data and update base salary ranges accordingly. We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience. In addition to our base salary offerings, we also provide equity grants for all new hires.