Own, extend, and improve production ML systems: training pipelines, evaluation frameworks, model serving infrastructure, and monitoring.
Optimize models for latency, cost, and reliability with a bias toward correctness in environments where errors are not recoverable
Translate research experiments into production-grade capability that solves real customer problems
Design and maintain evaluation and testing infrastructure to enable fast, high quality research and deployment
Integrate third-party model APIs and LLM orchestration frameworks into the platform
Support the deployment of agents into complex, high-stakes enterprise environments
Continuously improve system performance through disciplined benchmarking and iteration

Requirements

5+ years of experience building and maintaining ML systems in production
Track record of shipping ML systems where reliability and correctness were non-negotiable
Command of machine learning fundamentals and modern deep learning frameworks such as PyTorch or JAX
Strong skills in latency and cost optimization at scale
Strong programming skills in Python, with experience in at least one of C++, Rust, or Go
Comfort operating on a small team with minimal process, high ownership, and significant ambiguity
Demonstrated experience deploying ML solutions in real production environments serving end users or customers.

Benefits

Medical, dental, and vision coverage
Life and AD&D insurance
20 days of paid time off
9 sick days
401(k) plan with a company match

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdeep learningPyTorchJAXPythonC++RustGomodel optimizationbenchmarking

Soft Skills

ownershipadaptabilityproblem-solvingteam collaborationcommunicationdisciplineambiguity managementreliability focuscorrectness focushigh-stakes decision making