Staff Machine Learning Engineer

Staff Machine Learning EngineerLocation: Palo Alto, CA | On-site 5 days a weekAbout the Role & TeamAre you ready to bridge the gap between cutting-edge AI research and production-grade software engineering at massive enterprise scale?As an Staff Machine Learning Engineer, you will play a crucial role in ensuring the smooth operation and optimization of LLM-aided AI products. The team sits within the central Chief Data and Analytics Office, at the intersection of Computer Science and Computer Engineering, building scalable LLM-based products, reusable back-end APIs, and AI infrastructure powering 320,000+ employees organization-wide.In this role, you will lead the design and delivery of production architectures, accelerating ML adoption across the business and taking internal capabilities from 0 to 80% in days rather than months.Why Join?Massive Scale & Impact: Build tools (Chatbots, Text-to-Code, SQL agents, workflow automation) used by hundreds of thousands of users. Releases can touch 100,000+ users in week one, saving up to 30% of working time for operational teams.High Visibility: Report directly to executive leadership with direct Operating Committee confidence and high-profile ownership.Entrepreneurial Culture: Move fast in a startup-like environment backed by enterprise resources—tackle open problems on Thursday and deliver production-ready solutions by Monday.Top-Tier Talent: Collaborate with leading scientists and engineers from top tech labs and research institutions.Key ResponsibilitiesArchitect & Deploy: Combine vast data assets with cutting-edge LLMs and Multimodal LLMs, leading the design and delivery of production architectures.Bridge Science & Systems: Bridge scientific research and software engineering, requiring deep expertise in both domains.Scalable Infrastructure: Collaborate closely with cloud and SRE teams to design modular, reusable back-end APIs built for scalability.Ownership & Strategy: Act as a "Responsible Owner" for ML services in enterprise environments, writing clear, concise OKRs aligned with business expectations.Required QualificationsEducation: PhD in a quantitative discipline (e.g., Computer Science, Mathematics, Statistics).Hands-on Engineering: Experience in an individual contributor role in ML engineeringDistributed Systems: Hands-on experience in implementing distributed, multi-threaded, and scalable applications (using frameworks such as Ray, Horovod, DeepSpeed, etc.) and cloud/container environments (AWS, Kubernetes, Fargate).Core Fundamentals: Solid understanding of the fundamentals of statistics, optimization, and ML theory (focusing on NLP and/or Computer Vision), combined with excellent CS fundamentals and SDLC best practices.Preferred QualificationsLLM Optimization: Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models.Prompt Engineering & Reasoning: Hands-on knowledge of Chain-of-Thought, Tree-of-Thought, and Graph-of-Thought prompting strategies.Microservices & Orchestration: Experience designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray) and constructing batch/streaming microservices exposed as gRPC and/or GraphQL endpoints.Compensation & PackageTarget Base Compensation: $260,000-$310,000 + Bonus + Equity