Sr. Machine Learning Engineer

About the role

At Intel, our mission is to transform AI into something safer, more trustworthy, and respectful of human privacy by design. We build agentic AI that combines local and cloud intelligence—small, efficient models run directly on the user's machine (AI PC, edge, on-prem, and beyond) to keep data private and costs low, while powerful cloud models handle complex reasoning and problem-solving. This role sits at the intersection of research and engineering, focusing on agent harness research and model fine-tuning.

Responsibilities

• Build evaluation benchmarks and metrics.
• Develop and iterate on agent harness, including context engineering, agent memory, tools, and skills.
• Maintain and iterate on the post-training pipeline:
  • Design robust, reproducible training workflows from data ingestion and preprocessing through model checkpointing and deployment.
  • Develop RL environments and reward functions, including environments, reward signals, and verifiable reward frameworks for reasoning-intensive tasks.
• Debug and optimize training runs by profiling jobs, resolving bottlenecks, improving GPU utilization, and addressing numerical instability at multi-GPU scale.

What you’ll learn

• How post-training techniques improve model performance.
• How to make small models effective as agent backends.
• How model choices interact with runtime constraints on edge hardware.

Requirements

Minimum qualifications required:

• BS in Computer Science, Electrical Engineering, Mathematics, or a related STEM field.
• 8+ years of software development background.
• 4+ years of hands-on experience in machine learning engineering, data science, or ML research.
• Experience designing and building evaluation frameworks and benchmarks that measure model capability improvements and alignment quality.
• Proficiency in Python.
• Proficiency in LLM architectures, optimization, and model training dynamics.

Preferred Qualifications

• Master’s or PhD degree.
• Hands-on experience implementing and scaling the full post-training pipeline for language models, including supervised fine-tuning and reinforcement learning.
• Ability to own and drive a research agenda independently, generating hypotheses and prioritizing experiments without step-by-step supervision.
• Comfort with ambiguity and evolving problem definitions in fast-moving environments.
• 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.
• Collaborative work style and comfort with cross-functional teamwork.
• 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 quickly and adapt to changes with an open mind.

Requirements may be obtained through a combination of industry job experience, internships, and academic coursework, research, or class projects.

Benefits

Intel’s total rewards package includes competitive pay, stock bonuses, and comprehensive benefits such as health, retirement, and vacation programs. Additional details are available at Intel Benefits.

Pay

Annual salary range for this position in the U.S.: $195,200 – $361,200 USD. Individual pay is determined by job-related skills, experience, and relevant education or training, as well as work location.

Schedule

This role follows Intel’s hybrid work model, allowing employees to split time between working on-site at their assigned Intel location and off-site.