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About This Role
- Job Description 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 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 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 IMPORTANT: Please be informed that Intel is proactively trying to find candidates for this position which is frequently available at Intel.
- Please note that the position may not be available at this time.
- If you would be interested in this position should it become available, we would encourage you to apply, and our hiring team will be glad to contact you when/if relevant.
Requirements
- 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 Qualifications BS in CS, EE, Math or related STEM field 8+ years software development background 4+ years of hands-on experience in machine learning engineering, data science or ML research.
- Experiences designing and building evaluation frameworks and benchmarks that accurately measure model capability improvements and alignment quality 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.
- 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.
- Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.
- 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.
- It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation.
- Find out more about the benefits of working at Intel .
- Annual Salary Range for jobs which could be performed in the US: $195,200.00-361,200.00 USD 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.
- Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
Tools & Skills
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621 positions
Job ID
/job/US-California-Santa-Clara/Sr-Machine-Learning-Engineer_JR0285966
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