Staff Machine Learning Engineer

What you’ll do:

• Lead the design and implementation of large-scale recommendation and decisioning systems that power proactive advertiser and seller guidance across Ads Manager, Pinterest Business Assistant, Pinnacle, and sales productivity workflows.
• Build ML foundations for a unified context layer and context agent that transforms campaign, account, performance, market, workflow, and interaction data into reusable signals for agentic experiences.
• Own recommendation initiatives end-to-end, from problem framing, label and feedback design, feature pipelines, model development, and offline evaluation through production deployment, experimentation, and monitoring.
• Develop evaluation and feedback loops that measure recommendation quality, user trust, action rates, business impact, and failure modes, then use those learnings to continuously improve models and agent behavior.
• Apply modern ML techniques such as retrieval and ranking, embeddings, personalization, multi-objective optimization, contextual decisioning, and response modeling to business-critical advertiser and seller workflows.
• Use AI to accelerate analysis, prototyping, documentation, and experimentation while applying strong judgment, testing, data validation, and review to ensure correctness, reliability, privacy, and customer trust.
• Mentor engineers and raise the technical bar for ML development, experimentation rigor, responsible AI usage, and production-quality agentic systems across the organization.

What we’re looking for:

• 7+ years of experience building and deploying large-scale ML systems in production (e.g., ads ranking, recommendation, Agentic AI, or search), with strong end-to-end ownership from problem scoping through evaluation and experimentation, and solid software engineering skills in at least one modern language (e.g., Python, Java) and large-scale data systems.
• Degree in Computer Science, Mathematics, or a related technical field, or equivalent experience.
• Strong end-to-end ML ownership, including problem scoping, data and label design, feature engineering, model training, production deployment, offline/online evaluation, experimentation, and monitoring.
• Deep understanding of recommendation system architectures such as candidate generation, retrieval, ranking, re-ranking, embeddings, vector search, multi-task learning, calibration, contextual bandits, or reinforcement learning.
• Proven Staff-level technical leadership as a hands-on IC, setting technical direction and driving multi-quarter ML and systems roadmaps, including aligning stakeholders on priorities, trade-offs, and execution plans.
• Excellent cross-functional communication and collaboration skills, building strong partnerships with product, data science, infra, and partner ML teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senior stakeholders.
• Experience using AI coding assistants (e.g., Cursor, Claude Code) and LLM-powered productivity tools to accelerate development, experimentation, and data exploration, with a clear approach to validation, data protection, and critical review of AI-assisted work.

US based applicants only

$189,308—$389,753 USD