Interested in this AI/ML Engineer role at Pinterest?
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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.
The Team:
Pinterest helps people find inspiration and take action on it—connecting pinners with ideas and products they love. Within EPD, the Monetization org builds the ads and merchant ecosystem that funds Pinterest's business while protecting long\-term user experience. This Staff TPM role sits in Monetization as the TPM lead for Monetization Data Science, at the center of a highly cross\-functional network (Product, Engineering, Design, Sales, PMM, Core, Platforms, Data). What's exciting is the team's explicit shift toward a "data\-driven monetization engine": unifying fragmented data into a trusted SSOT, building an end\-to\-end input metrics funnel, enabling advanced segmentation, and democratizing analytics so teams can move faster and make better decisions with shared context.
What you'll do:
- Lead the Monetization DS execution roadmap: drive the integrated plan across the four strategic pillars (SSOT \+ funnel, segmentation, input\-metrics cadence, democratized analytics) with clear milestones and success measures.
- Productionalize our DS strategy: coordinate Platforms/Data Eng \+ Monetization Eng \+ DS to productionalize core tables, governance, reliability, and scale beyond DS\-owned pipelines.
- Enable new instrumentation: partner with Engineering to close observability gaps (especially delivery funnel instrumentation) so full\-funnel survivability can be analyzed reliably.
- Drive workflow automation: reduce manual human intervention in recurring data workflows and program operations; build durable mechanisms for monitoring, alerting, and dependency tracking.
- Scale self\-serve and democratization: deliver partner\-facing tooling (dashboards / analytics surfaces) that makes staples the common language and supports fast diagnostics and opportunity mining.
- Operationalize input metrics: establish/upgrade business review cadences so teams set goals and are accountable for moving controllable input metrics (not just reporting revenue outcomes).
- Drive targeted deep dives: structure and execute cross\-functional deep\-dive programs (e.g., influencer population, auction density/demand) with clear hypotheses, decision asks, and downstream action plans.
- Use GenAI as the default operating model for EP PgM execution—producing AI\-assisted first drafts of core program artifacts, modernizing high\-toil workflows into AI\-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk \& dependency tracking), and synthesizing signals to proactively surface risks, decision/trade\-offs, and escalation paths.
- Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants ("vibe coding").
- Follow Pinterest AI guidance for risk, governance, and safety\-by\-design: appropriately handle sensitive data, validate AI\-generated outputs, document assumptions/limits, and ensure AI\-assisted workflows meet applicable policy/compliance expectations before broad adoption.
What we're looking for:
- Staff\-level TPM scope and behaviors: proven ability to independently own multi\-team, multi\-quarter technical programs, including resolving ambiguity, driving decisions, and delivering outcomes through influence.
- Deep cross\-functional leadership: strong partnership with Product and Engineering plus ability to align Design, Sales, PMM, Core, Platforms, and Data on sequencing, tradeoffs, and adoption.
- Data platform \+ metrics judgment: experience building trusted metrics/SSOT and operational cadences that shift org behavior toward leading indicators and fast diagnosis.
- Mechanism builder, not "process administrator": track record of creating durable operating systems (cadence, dashboards, decision logs, RACI/DRIs) that reduce toil and increase velocity.
- Excellent risk and dependency management: anticipates cross\-org failure modes, keeps stakeholders aligned with crisp comms, and escalates with clear options and recommendations.
- AI\-first execution mindset: demonstrated ability to use GenAI to accelerate planning, program operations, and stakeholder communications—starting with AI drafts and applying strong judgment to validate, refine, and drive decisions.
- Workflow design, AI fluency, data \& insights orientation: experience turning repeatable program work into durable, low\-toil mechanisms and improving decision\-making by using GenAI (e.g., strong prompting, vibe coding lightweight scripts/tools, dashboards, data analysis and leveraging agents where appropriate)
- Safety\-by\-design AI fluency: experience operating within AI governance expectations (risk assessment, data handling, model/output validation, auditability/traceability) and proactively identifying where AI use is not appropriate or requires additional controls.
- Bachelor's degree in Computer Science, Engineering, a related field or equivalent experience.
Relocation Statement:
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
In\-Office Requirement Statement:
- We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day\-to\-day can vary based on the needs of each organization or role.
- This role will need to be in the office for in\-person collaboration 1\-2 times every 6\-months and therefore can be situated anywhere in the country.
\#LI\-REMOTE
\#LI\-JD3
Our Commitment to Inclusion:
Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.
*By submitting this application, I certify that all information submitted in my application and throughout the hiring process is true, accurate, and complete to the best of my knowledge. I understand that any false statement, omission, or misrepresentation may disqualify me from employment consideration or result in termination if discovered after hire.*
Salary Context
This $145K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pinterest, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 20% above the category median. Disclosed range: $145K to $300K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Pinterest AI Hiring
Pinterest has 5 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span San Francisco, CA, US, Remote, US. Compensation range: $300K - $389K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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