Applied Machine Learning Engineer (llms & Rl)

Intel Intel · Semiconductors · California, Santa Clara, United States +2

Applied Machine Learning Engineer focused on fine-tuning large language models (LLMs) and Reinforcement Learning (RL). Responsibilities include designing and maintaining post-training pipelines, developing RL environments and reward models, debugging and scaling distributed training, and designing experiments and evaluation metrics.

What you'd actually do

  1. Design and maintain post‑training pipelines, from data ingestion through deployment
  2. Develop reinforcement learning environments, reward models, and evaluation signals
  3. Debug, optimize, and scale distributed training workloads
  4. Design and execute research experiments and ablation studies
  5. Develop benchmarks and evaluation metrics for model capability and alignment

Skills

Required

  • Python/C++
  • LLM architectures
  • optimization
  • model training fine tuning evaluation technics
  • machine learning engineering
  • data science
  • ML research or modeling fine tuning

Nice to have

  • Masters or PhD degrees
  • supervised fine tuning
  • reinforcement learning
  • evaluation frameworks and benchmarks
  • model distillation
  • quantization

What the JD emphasized

  • fine tuning
  • evaluation
  • alignment quality

Other signals

  • fine-tuning LLMs
  • RL environments and reward models
  • distributed training workloads
  • benchmarks and evaluation metrics