San Francisco, CA, US; Remote, US (United States) · Remote
Full-time
Mid level
6 hours ago
Verified apply link — checked 1 hour ago
Salary undisclosed
About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . Our Retrieval team builds shared machine learning capabilities that help Pinterest connect people with relevant ideas. We are looking for a Staff Machine Learning Engineer to advance how we develop those capabilities through agentic AI. Your primary focus will be Reflex, our effort to automate and accelerate recommendation research and development. You will own a major technical area, building agents that investigate opportunities, implement changes, run experiments and learn from their results. You will work closely with other technical leaders while remaining hands-on with the hardest engineering and modeling problems. You will also contribute to generative retrieval, with a particular focus on Semantic IDs: learning structured representations of content that recommendation models can generate and use to retrieve relevant items. This role combines applied AI research, production engineering and technical leadership, with opportunities to improve both our recommendation models and the way engineers build them. What you'll do: Own and advance a major technical area in automated recommendation research within Reflex, collaborating with other technical leads and partner teams. Build LLM agents that connect hypothesis generation, implementation, experiment execution, evaluation and learning from previous results. Develop rigorous evaluations for agent behavior and research outputs, and use them to improve quality, reliability and efficiency. Contribute to generative retrieval models, particularly Semantic ID learning, evaluation and integration into production recommendation systems. Take promising ideas from research and prototypes through offline evaluation, online experimentation and production launches. Build reliable integrations with ML development infrastructure and make informed tradeoffs across model quality, iteration speed, compute cost and operational complexity. Raise technical quality through hands-on implementation, design and code reviews, documentation and mentorship. Work with research, recommendation and ML infrastructure teams to shape technical approaches, resolve dependencies and support adoption. Identify and apply relevant advances from research, and contribute new methods where existing approaches fall short. What we're looking for: Bachelor's in Computer Science, Machine Learning or a related field, or equivalent practical experience. Minimum 8 years of experience building and shipping ML or AI systems, including substantial hands-on engineering and technical leadership. Demonstrated experience building LLM agents or other agentic AI systems for complex tasks, with a clear approach to evaluating and improving their performance. Strong ML fundamentals and depth in one or more relevant areas, such as applied LLMs, representation learning, recommendation systems, retrieval or ML systems
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