Machine Learning Engineer
Core
Design and implement algorithms for agent harness and post-training pipelines, develop RL environments and reward models, and conduct training runs to improve model capabilities for agentic applications.
Role type
Machine Learning Engineer / Data Scientist (Research & Engineering)
Builds
Agent harness, post-training pipelines, RL environments, evaluation benchmarks
Domain
AI / Machine Learning / Agentic Systems
Deliverable
production ML models
Required skills
Python, LLM architectures, model training dynamics, software development, data ingestion and preprocessing, model checkpointing, GPU optimization, numerical stability debugging
Preferred skills
Post-training pipeline implementation (SFT, RL), evaluation framework design, independent research agenda ownership, ambiguity tolerance, deep codebase debugging, research-engineering balance
Technologies
Python, LLMs, multi-GPU training environments
Responsibilities
Build evaluation benchmarks and metrics; Build and iterate on agent harness including context engineering and agent memory; Build and maintain post-training pipelines from data ingestion to deployment; Design RL environments and reward functions; Debug and optimize training runs including profiling and GPU utilization
Seniority
Mid-Senior, hands-on IC
