Machine Learning Engineer

Job Details: Job Description: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 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 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 scho #J-18808-Ljbffr