Machine Learning Engineer

Intel

Phoenix, Arizona, United States

Phoenix, Arizona, United States

About

Our Mission At Intel, our journey is to transform AI into something safer, more trustworthy, and respectful of human privacy by design. We believe transformative AI should have a positive impact on people—powerful in capability, yet honest about its limits and protective of the data and resources it touches. To get there, we build agentic AI that combines the best of local and cloud intelligence — private, affordable, and sustainable by design. Small, efficient models run directly on the user's machine (AI PC, edge, on-prem, and beyond), keeping data private and token costs low, while powerful cloud models handle the hardest work: planning, reasoning, and complex problem‑solving. Today, neither approach can deliver this alone. Together, they give people real capability without compromise—data stays private, spend stays predictable, and energy use stays in check. We're building intelligence that scales without sacrificing trust, cost, or the planet—because the future of AI should belong to the people it serves. Role Summary 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: the ideal candidate designs and implements algorithms for agent harness and post‑training pipelines, develops RL environments and reward models, and conducts training runs to improve model capabilities for agentic applications. What You’ll Do Build evaluation benchmarks and metrics Build and iterate on agent harness, including context engineering, agent memory, tools, and 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 What You’ll Learn / Grow Into Curiosity is required. You will develop: How post‑training techniques actually move model performance How to make small models punch above their weight as agent backends How model choices interact with runtime constraints on edge hardware Required 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 experience 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 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. Compensation Annual salary range for jobs which could be performed in the US: $170,500.00–315,490.00USD. The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Job Details Job type: Shift 1 (United States of America). Primary location: US, California, Santa Clara. Additional locations: US, Arizona, Phoenix; US, California, Folsom; US, Oregon, Hillsboro. Business group: The Client Computing Group (CCG) is responsible for driving business strategy and product development for Intel's PC products and platforms. Work Model 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. EEO Statement All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
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Phoenix, Arizona, United States

Languages

English

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