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Tech Stack
Tools & technologiesCloudGoPython
About the role
Key responsibilities & impact- Own the engineering work that moves ML capabilities from research into production. You write the code, you ship the feature, you are accountable for it working.
- Work across the AI/ML team to understand their work and make sound engineering judgments about what is ready to ship and how to get it there.
- Build and maintain model serving and evaluation infrastructure. You understand the stack deeply, including how inference works, why serving choices matter, and what the tradeoffs are.
- Lead implementation of ML-driven features, coordinating with the research team and the rest of engineering to get things shipped.
- Debug production systems including edge cases and failure modes in ML pipelines and retrieval systems.
- Establish and improve observability, debugging, and testing practices for ML systems.
- Build and maintain evaluation systems, including datasets, scoring approaches, and repeatable testing to detect regressions.
- Improve the reliability, testability, and maintainability of the ML codebase without slowing down iteration.
- Use AI coding tools fluently as a core part of your daily workflow, with the judgment to evaluate what they produce and catch errors before they ship.
Requirements
What you’ll need- Strong software engineering background with a track record of owning and shipping production systems end to end.
- Genuine MLOps depth. You have run model serving and/or evaluation infrastructure in production, understand systems like KServe, and have real opinions about the serving stack. You are not someone whose experience stops at configuring managed cloud ML services. You understand the serving stack deeply enough to make tradeoffs, debug failures, and improve it.
- Enough ML knowledge to be a real partner to the AI/ML team. You can follow what they are building, ask the right questions, and make sound engineering decisions about productionizing it. You are not training models.
- Experience with LLMs, RAG systems, or agent-based workflows at a level deeper than stringing together API calls.
- Strong proficiency in Python. Comfortable in Go or willing to get there quickly.
- Experience integrating multiple systems, APIs, and data sources into cohesive product functionality.
- Experience designing or working with evaluation systems for ML quality.
- Experience debugging production ML systems including handling edge cases and failure modes.
- Daily, fluent use of AI coding tools as a core part of your engineering workflow.
Benefits
Comp & perks- **Early-Stage Ownership:** Join at the ground floor of a company with real traction and momentum.
- **Empowered Culture:** We value autonomy, candor, and craft. You'll be trusted to lead.
- **Cutting-Edge Tech:** Work with the latest in AI, backend systems, and intelligent infrastructure.
- **Meaningful Impact:** Shape a platform that transforms how organizations activate knowledge.
- **Holistic Benefits:** Competitive comp, equity, 100% paid healthcare, 401K, flexible PTO, and a team that truly cares.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
software engineeringMLOpsmodel servingevaluation infrastructurePythonGodebuggingML pipelinesevaluation systemsAI coding tools
Soft Skills
accountabilitycollaborationsound engineering judgmentproblem-solvingcommunication