Machine Learning Lead Engineer, Early Stage Supply Chain Project

Machine Learning Lead Engineer, Early Stage Supply Chain Project

Software Engineering Mountain View, CA (HQ)

We’re X’s moonshot for the global supply chain, applying AI to reduce waste, fragility, and inefficiency. We’re an agile team of experienced AI researchers, software engineers, and entrepreneurs working with visionary partners and customers around the world to make sure everyone has sustainable and reliable access to the food, medicine, clothing, and other goods and services they need.

We’re looking for people who are passionate about revolutionizing the global supply chain to join us at this critical growth stage. You’ll be part of a fast-paced team that has the agility and impact of an early stage company, while building on world-class Google AI technology.

You’ll play a pivotal role in designing, architecting, building, and deploying cutting-edge AI systems. This role requires a deep passion for problem-solving and experimentation, moving across the full range of applied machine learning tasks from initial prototyping to building full-scale, robust solutions. While you will primarily focus on core product development, you will also serve as a critical bridge between cutting-edge AI research and real-world enterprise applications.

• Develop and deliver high-quality, production-grade AI enterprise software, from ideation and architectural design to development, testing, deployment, and ongoing maintenance in complex enterprise environments
• Help drive and execute key decisions on software architecture and features
• Develop experiences that inspire confidence, delight and resonate with end users, and encourage adoption and trust
• Enhance AI reasoning by innovating on how Foundation Models are utilized, improving internal workflows, and developing/integrating specialized tools
• Explore, apply, and innovate state-of-the-art LLM and machine learning techniques
• Lead and mentor technical initiatives:
  Guide project execution, provide technical leadership, and cultivate a strong, high-performing team
• Ensure responsible AI development:
  Implement robust safety principles, including sandboxing and traceability of self-modifications, to mitigate risks and ensure alignment with business objectives and ethical guidelines

• MS/PhD degree or equivalent practical experience in machine learning, computer science, or a related quantitative field
• 7+ years of professional experience designing, developing, maintaining, and releasing production-grade enterprise/business software
• Proficiency in Python development, with experience in the latest Python tool chains and frameworks, including developing for various platforms (e.g., full-stack web applications, backend services, data pipelines)
• Experience integrating AI into enterprise software environments, including Large Language Models (LLMs), Foundation Models, multi-agent system architectures and their orchestration agentic behaviors, tool use, and reasoning capabilities
• Experience with collaborative software development workflows and platforms including Git Hub, agile development/scrum, unit testing, CI/CD, and production operations
• Experience using LLMs to optimize software development processes and workflows
• Experience integrating enterprise legacy systems, including ERP integration (e.g., SAP, Oracle)
• Experience with deploying and managing applications on major cloud platforms (e.g., Google Cloud Platform, AWS, Azure) and an understanding of underlying infrastructure primitives (e.g., Kubernetes, Terraform)
• Proven leadership, mentorship, or lead engineer experience for small technical teams with a track record of owning outcomes end-to-end
• Excellent written and verbal communication skills, able to explain complex technical concepts to technical and non-technical audiences
• Strong organizational and planning skills
• Ability to think strategically and tactically to drive complex initiatives to completion
• Ability to thrive in a fast-changing environment

• Experience in startups or small-team environments
• Research papers or public-facing documents/presentations showing technical…