Applied ML Engineer
Core
Turn ML research ideas into rigorous experiments, measurable evidence, and reliable production systems for model evaluation and verification.
Role type
Applied ML Engineer (Research-to-Production)
Builds
Production-grade evaluation infrastructure, experiment runners, verification workflows, and user-facing product interfaces 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, experimental design
Preferred skills
Model provenance/verification, activation analysis, adversarial evaluation, inference infrastructure (DSPy, LiteLLM, Ray), open-weight model serving
Technologies
PyTorch, Hugging Face, React, TypeScript, PostgreSQL, vLLM, Ray, Temporal
Responsibilities
Reproduce and evaluate ML research methods using open-weight models; Design evaluation datasets, probes, and baselines; Build and extend evaluation infrastructure for reproducibility; Turn research workflows into intuitive product experiences; Investigate model behavior under fine-tuning, merging, and quantization; Produce clear technical reports separating evidence from interpretation.
Seniority
Mid-Senior, hands-on IC