Applied ML Engineer
Core
Transform emerging ML techniques into practical systems by reproducing research methods, designing rigorous experiments, and building production-grade evaluation infrastructure for model verification and safety.
Role type
Applied ML Engineer (Research-to-Production)
Builds
Production-grade evaluation tooling, experiment runners, verification workflows, and user-facing dashboards for model analysis.
Domain
Machine Learning, Model Verification, Safety, LLM Inference
Deliverable
production ML models | product features | dashboards & analysis
Required skills
Python engineering, PyTorch, Hugging Face Transformers, ML evaluation design, statistical analysis, software engineering (APIs, async jobs, databases), React/TypeScript, model internals analysis, experimental design
Preferred skills
Model provenance, fingerprinting, watermarking, distillation detection, red-teaming, activation probing, DSPy, LiteLLM, Temporal, Ray, vLLM, Next.js, GPU model serving, adversarial evaluation
Technologies
PyTorch, Hugging Face, React, TypeScript, PostgreSQL, pgvector, Next.js, Ray, vLLM, Temporal, DSPy, LiteLLM
Responsibilities
Reproduce and evaluate machine learning research methods; Design evaluation datasets, probes, and experiment harnesses; Build and extend evaluation infrastructure for reproducibility; Turn research workflows into intuitive product experiences; Investigate verification methods under model modifications (fine-tuning, merging, quantization); Produce clear technical reports separating evidence from interpretation; Deliver production-quality systems with APIs and observability.
Seniority
Mid-Senior, hands-on IC