Applied ML Engineer
Core
Transform emerging ML research techniques into practical, production-grade systems for model evaluation, verification, and inference.
Role type
Applied ML Engineer (Research-to-Production)
Builds
Production evaluation infrastructure, experiment runners, verification tools, and user-facing dashboards for model analysis.
Domain
Machine Learning, Model Verification, LLM Inference, Research Engineering
Required skills
Python, PyTorch, Hugging Face Transformers, ML evaluation design, statistical analysis, software engineering (APIs, async jobs, databases), React/TypeScript, open-weight model familiarity
Preferred skills
Model provenance/verification, activation analysis, adversarial evaluation, DSPy/LiteLLM/Temporal/Ray/vLLM, Next.js, GPU model serving
Technologies
PyTorch, Hugging Face, React, TypeScript, PostgreSQL, pgvector, Next.js, Ray, vLLM, Temporal, DSPy, LiteLLM
Responsibilities
Reproduce and evaluate ML research methods using open-weight models; Design evaluation datasets, probes, and experiment harnesses; Build production-grade tooling for repeatable experiments; Investigate model behavior under fine-tuning, merging, and quantization; Produce technical reports separating evidence from interpretation; Deliver production-quality systems with APIs and observability.
Seniority
Mid-Senior, hands-on IC