Machine Learning Engineer I/II, Applied AI
Core
Build production-oriented ML systems to adapt frontier AI models for customer-specific scientific workflows, focusing on model training, evaluation, and integration.
Role type
Applied Machine Learning Engineer (IC)
Builds
Production ML systems, evaluation loops, and model adaptation tooling for scientific use cases
Domain
AI/ML applied to science (medicine, materials, energy)
Deliverable
production ML models
Required skills
Model training and adaptation (SFT, RL/DPO/PPO), Python, PyTorch/JAX/TensorFlow, experiment design, model debugging, cross-functional collaboration
Preferred skills
RL post-training (RLHF/GRPO), MoE architectures, RAG, agentic workflows, scientific domain experience
Technologies
PyTorch, JAX, TensorFlow, LLMs, multi-modal models
Responsibilities
Post-train models using SFT and RL approaches, build evaluation loops to measure model quality, design experiments for performance improvement, debug model failures using traces and feedback, partner with researchers to translate improvements into capabilities, integrate model behavior into end-to-end product workflows
Seniority
Mid-level IC (I/II)