Job Details:Job Description:Role 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 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 intoCuriosity 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
Qualifications: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 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.
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 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, 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.
BenefitsWe offer a total compensation package that ranks among the best in the industry. 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: $170,500.00-315,490.00 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.
Work Model for this RoleThis 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. * Job posting details (such as work model, location or time type) are subject to change.
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
