Machine Learning Engineer

Intel Intel · Semiconductors · California, Santa Clara, United States +3

Machine Learning Engineer/Data Scientist role focused on agent harness research and model fine-tuning, involving building evaluation benchmarks, iterating on agent harness components (context, memory, tools, skills), developing and maintaining post-training pipelines, designing RL environments and reward functions, and optimizing training runs. The role emphasizes the intersection of research and engineering for agentic applications.

What you'd actually do

  1. Build evaluation benchmarks and metrics
  2. Build and iterate on agent harness, including context engineering, agent memory, tools, skills.
  3. Build, maintain, and iterate on the post-training pipeline: Develop robust, reproducible training workflows from data ingestion and preprocessing through model checkpointing and deployment
  4. Design RL environments and reward functions — Develop environments, reward signals, and verifiable reward frameworks for training models on reasoning-intensive tasks.
  5. Debug and optimize training runs — Profile training jobs, resolve bottlenecks, improve GPU utilization, and address numerical instability at multi-GPU scale

Skills

Required

  • Python
  • LLM architectures, optimization and model training dynamics
  • software development background
  • machine learning engineering, data science or ML research

Nice to have

  • Masters or PhD degrees
  • implementing and scaling the full post-training pipeline for language models including supervised fine tuning and reinforcement learning
  • designing and building evaluation frameworks and benchmarks
  • Ability to own and drive a research agenda independently
  • Ambiguity tolerance
  • Debug-first mindset
  • Research-engineering balance
  • Collaborative work style
  • Clear technical communication
  • Ability to learn new technologies fast

What the JD emphasized

  • post-training pipeline
  • evaluation frameworks and benchmarks
  • own and drive a research agenda independently
  • Ambiguity tolerance
  • Debug-first mindset
  • Research-engineering balance

Other signals

  • agentic AI
  • local and cloud intelligence
  • private, affordable, and sustainable AI
  • agent harness research
  • model fine tuning
  • RL environments and reward models
  • post-training pipeline