Staff Applied Machine Learning Scientist- GenAI

Position Details 

Grainger’s Sales Machine Learning team is seeking a Staff Applied Machine Learning Scientist to serve as a technical leader for Generative AI and agentic AI applications across the Sales domain. In this role, you will help design and build Grainger’s Sales AI platform, enabling 3,000+ sellers to work more productively, serve customers more effectively, and drive measurable business growth. 

You will lead the development of scalable AI systems that combine LLMs, machine learning models, retrieval, tools, APIs, workflow orchestration, evaluation, and monitoring. The work focuses on building production-ready virtual assistants and deep agents across mobile and web experiences. You will set the technical direction, establish patterns for reliable and governed AI systems, mentor engineers, and partner with product, engineering, design, and business stakeholders to turn high-value Sales opportunities into production-ready AI capabilities. 

The work location is Chicago, IL on a hybrid basis (8 days per month). We will consider highly qualified candidates who are remote, provided they can come onsite during orientation and travel for team meetings, estimated at once per quarter. 
 

You Will 

• Design, develop, and maintain both traditional and cutting-edge machine learning models that solve real-world business problems—ensuring scalability, efficiency, and measurable impact 

• Develop applied GenAI capabilities across web and mobile experiences, including seller-facing digital assistants, conversational Q&A, task automation, summarization, workflow guidance, recommendations, and next-best-action intelligence. 

• Design AI systems with appropriate controls for reliability, privacy, security, governance, observability, and human oversight. 

• Collaborate cross-functionally with ML scientists, data/platform/software engineers, UX designers, and product managers to seamlessly integrate models into production systems 

• Establish and champion best practices for model development, deployment, and lifecycle management. Clearly articulate technical concepts and create visual representations to communicate your work and its impact 

• Mentor and empower engineers and scientists, fostering a culture of continuous learning and shared growth. Share knowledge through internal documentation, presentations, and external community engagement 

• Translate customer and product requirements into ML strategies that deliver tangible, data-driven outcomes 

• Contribute to robust, maintainable codebases and scalable ML infrastructure that supports long-term innovation 

• Stay at the forefront of research and technology, continuously evaluating and applying emerging methods that add value to the organization 

You Have 

• Master's degree in computer science, data science, statistics, mathematics, or a closely related field  

• 5+ years of experience designing and building scalable AI/ML applications and systems in cloud environments. 

• Experience developing virtual assistants, digital assistants, conversational AI applications, agentic workflows, recommendation systems, or AI-powered decision-support tools. 

• Production-grade implementation experience using PyTorch, HuggingFace, MLFlow 

• Experience using frameworks to build LLM-based applications or agentic workflows, such as LangGraph, LangSmith, LangChain, LlamaIndex, or similar. 

• Experience designing AI evaluation systems, including offline evaluation, online monitoring, hallucination and quality checks, latency measurement, user feedback loops, and business impact tracking. 

• Experience with the end-to-end software development lifecycle, including CI/CD pipelines, Kubernetes-based deployments, testing, monitoring, alerting, backend systems, APIs, and production support. 

• Strong Python fundamentals, with experience using modern ML, data, and LLM application development libraries. 

• Ability to provide technical leadership, influence architecture, mentor team members, and communicate complex AI/ML concepts to technical and non-technical audiences. 

Preferred 

• PhD computer science, data science, statistics, mathematics, or a closely related field 

• Experience with deep agent or multi-agent architectures, including planning, reasoning, tool use, tool dispatch, error recovery, session state, memory, sub-agent coordination, and feedback loops. 

• Experience designing AI agent skill systems, including reusable capability packages, skill registries, versioning, security vetting, governance controls, and lifecycle management. 

• Experience building digital assistant experiences for web, mobile, CRM, seller productivity, customer support, or enterprise workflow applications. 

• Production model serving experience with vLLM, Triton Inference Server, TensorRT, Ray Serve, TorchServe, or similar low-latency serving infrastructure.