Staff Machine Learning Engineer, Content Mining

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 theflexibility to do your best work. Creating a career you love? It’s Possible.

Content Mining identifies the best sources to acquire content for Pinterest (websites, merchants, social accounts), optimizes how we acquire it, and extracts structured attributes from that content at high scale. Our work powers inspiring, accurate, and engaging Pins. This is a high performing end to end ML team, with a recent paper in KDD: “Cross-Domain Web Information Extraction at Pinterest”.We’re hiring a Staff ML Engineer to serve as the technical lead for a 5‑engineer team (4 MLEs + you). As a tech lead, you will define the multi‑quarter technical vision and roadmap, lead execution and mentor engineers. The majority of your time will be spent with hands‑on designing, training, and shipping ML systems—especially LLM/NLP models for extraction and

What you’ll do:

• Technical leadership
• Own the long‑term architecture, roadmap, and execution for source discovery, acquisition optimization, and content understanding.
• Lead design reviews, set engineering standards, and drive cross‑team alignment with Product, Data, and Infra.
• Mentor and uplevel MLEs through technical direction, pairing, and reviews.
• Modeling and systems
• Train/fine‑tune LLMs and NLP models for classification, extraction, and instruction‑following; design eval loops and guardrails.
• Design features and frameworks for sharing features across models.
• Productionize models for large-scale inference; drive latency, reliability, and cost efficiency (quantization, distillation, caching).
• Data, experimentation, and quality
• Establish offline/online evaluation, gold sets, and automated regressions; run A/B and canary/shadow launches.
• Work with human and automated labeling sources to define data labeling standards.
• Partner on data strategy, labeling/weak supervision, and feedback loops to expand coverage and improve precision/recall.
• Operational excellence
• Define and meet SLOs for data quality, model performance, and serving reliability; lead incident playbooks and postmortems.
• Measure and drive downstream impact on revenue and engagement.

What we're looking for:

• 5+ years building ML products end‑to‑end, including 2+ years as a tech lead driving multi‑quarter roadmaps and cross‑functional execution.
• Deep hands‑on experience with NLP/LLM training and inference (PyTorch, Python); strong grounding in evaluation, prompt/data design, and fine‑tuning.
• Proven track record shipping models at scale: feature/data pipelines, online serving, monitoring/observability, and cost/perf trade‑offs.
• Strong software engineering in Python with an eye for software engineering best practices.
• Experience mentoring senior engineers and influencing partner teams.
• Masters or PhD in ML related studies.
• LLM efficiency techniques (LoRA/adapters, distillation, quantization, prompt caching) and cost control strategies.
• MLOps at scale with tools like Airflow, Spark/Presto, Triton, vLLM.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit ourPinFlexpage to learn more about our working model.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the province of Ontario.

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