Machine Learning Engineer

Job Details:Job Description: Our MissionAt 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 servesRole SummaryWe 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 doWork in a dynamic team to:Build evaluation benchmarks and metricsBuild 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 deploymentDesign 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 scaleWhat you’ll learn / grow intoCuriosity is required. You will develop:How post-training techniques actually move model performanceHow to make small models punch above their weight as agent backendsHow model choices interact with runtime constraints on edge hardwareQualifications:Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.You must possess the minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.Required QualificationsBS in CS, EE, Math or related STEM field5+ years software development background2+ years of hands-on experience in machine learning engineering, data science or ML researchProficient in PythonProficient in LLM architectures, optimization and model training dynamics.Preferred QualificationsMasters 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 qualityAbility 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.Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.Benefits at IntelOur 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. Intel reserves the right to modify, change or discontinue benefit plans at any time in its sole discretion.          Job Type:Shift:Shift 1 (United States of America)Primary Location: US, California, Santa ClaraAdditional Locations:US, Arizona, Phoenix, US, California, Folsom, US, Oregon, HillsboroBusiness group:The Client Computing Group (CCG) is responsible for driving business strategy and product development for Intel's PC products and platforms, spanning form factors such as notebooks, desktops, 2 in 1s, all in ones. Working with our partners across the industry, we intend to deliver purposeful computing experiences that unlock people's potential - allowing each person use our products to focus, create and connect in ways that matter most to them.Posting 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