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
Deliverable
production ML models | product features | infrastructure
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