Machine Learning Engineer

About the role

We are seeking a Machine Learning Engineer / Data Scientist to join our team, working on agent harness research and model fine tuning. This role sits at the intersection of research and engineering.

Responsibilities

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

Qualifications

• BS in CS, EE, Math or related STEM field
• 5+ years software development background
• 2+ years of hands-on experience in machine learning engineering, data science or ML research
• Proficient in Python
• Proficient in LLM architectures, optimization and model training dynamics

Preferred Qualifications

• Masters or PhD degrees are preferred
• Hands-on experience implementing and scaling the full post-training pipeline for language models including supervised fine tuning and reinforcement learning
• Previous experiences designing and building evaluation frameworks and benchmarks that accurately measure model capability improvements and alignment quality
• Ability to own and drive a research agenda independently, generating hypotheses and prioritizing experiments without step-by-step supervision
• Ambiguity tolerance: Comfortable making progress in fast-moving environments where problem definitions evolve and priorities shift
• Debug-first mindset: Willingness and skill to dive deeply into large, complex ML codebases to isolate and fix subtle issues
• Research-engineering balance: Ability to produce production-quality implementations of novel research ideas, balancing rigor with speed
• Collaborative work style: Comfort with cross-functional collaboration
• Clear technical communication: Ability to explain research results, architectural decisions, and trade-offs to both technical and non-technical stakeholders
• Ability to learn new technologies fast and adapt to changes with open-mindedness

Benefits

At Intel, our total rewards package goes above and beyond just a paycheck. Whether you're looking to build your career, improve your health, or protect your wealth, we offer generous benefits to help you achieve your goals. Go to Intel Benefits | Intel Careers for details of benefits available to you.

Pay

The annual salary range for jobs which could be performed in the US is $170,500.00 - 315,490.00 USD. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Schedule

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site.