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
Deliverable
production ML models | product features | dashboards & analysis
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