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 Technical Program Managers are proactive owners with technical expertise. They are aligned to the highest level company priorities to support successful delivery of programs within their teams. The Platform T/PgM Team is responsible for overall program governance in Infrastructure, Infra Finance, Data Engineering and Security, as well as Compliance and managing our cloud budget.
What you'll do:
- As a Staff Technical Program Manager focusing on cross\-engineering special projects, you will identify, scope, champion, and land key strategic projects important to advancing Pinterest's platform and underpinning.
- Lead Strategic ML/AI Platform Programs — Identify, scope, champion, and deliver high\-impact, cross\-engineering initiatives critical to advancing Pinterest's ML/AI Platform, GenAI infrastructure, and Agent Platform; owning outcomes from ambiguous opportunity through measurable execution.
- Operate with an AI\-First Execution Mindset — Use GenAI as the default operating model for program execution; producing AI\-assisted first drafts of core program artifacts, modernizing high\-toil workflows (e.g., intake triage, status synthesis, risk and dependency tracking, action/decision extraction), and synthesizing signals to proactively surface risks, tradeoffs, and escalation paths.
- Prototype AI\-Augmented Tools \& Workflows — Leverage AI coding assistants to build lightweight solutions that improve decision\-making and reduce friction — including dashboards, data analysis, process helpers, and internal tooling; using a "vibe coding" approach to move fast and validate value early.
- Apply Safety\-by\-Design AI Governance — Follow Pinterest's AI guidance for risk, governance, and compliance; appropriately handling sensitive data, validating AI\-generated outputs, documenting assumptions and limitations, and ensuring AI\-assisted workflows meet applicable policy and safety expectations before broad adoption.
- Drive Complex Technical Decisions Across Teams — Partner closely with technical domain owners across MLP, Agent Platform (Helix), Core Infra, and Ads to proactively identify, frame, and resolve complex cross\-team technical decisions; sequencing risky elements to fail fast and reduce downstream program exposure.
- Translate Strategy into Execution — Convert strategic platform priorities into structured, milestone\-driven execution plans; operating at both breadth (big picture vision) and depth (driving granular details with engineering teams) to ensure nothing falls through the cracks.
- Shape Planning \& Influence Roadmaps — Serve as a thought leader to cross\-functional stakeholders during annual and quarterly planning cycles; influencing product and platform roadmap decisions with data\-informed perspectives and a deep understanding of organizational priorities and technical tradeoffs.
- Build Scalable Processes \& Best Practices — Define, institutionalize, and continuously improve program management processes, frameworks, and tooling; elevating organizational execution capability and enabling teams to operate with greater agility, consistency, and efficiency at scale.
- Drive Cross\-Functional Alignment \& Communication — Build transparent, adaptive communication channels across Engineering, ML Research, Data Science, Product, Security, and Legal; ensuring stakeholders at all levels; from ICs to executives, have timely, accurate visibility into priorities, status, risks, and decisions.
- Own Program Outcomes End\-to\-End — Take full accountability for the success of high\-impact programs, ensuring measurable delivery through rigorous planning, proactive risk mitigation, and relentless follow\-through; representing program interests at the highest levels of the organization.
What we're looking for:
- Experienced Technical Program Manager — 8\+ years of TPM or related experience, with a Bachelor's degree in Computer Science or equivalent. With a proven track record of leading and delivering complex, multi\-year initiatives spanning multiple engineering teams and organizations.
- AI\-First Execution Mindset — Demonstrated ability to use GenAI to meaningfully accelerate program planning, operations, and stakeholder communication; starting from AI\-generated drafts and applying strong judgment to validate, refine, and drive decisions rather than defaulting to manual, high\-toil approaches.
- Workflow Design \& AI Fluency — Experience turning repeatable program work into durable, low\-toil mechanisms; including strong prompting, vibe coding lightweight scripts and tools, building dashboards, and leveraging agents to improve decision\-making and operational throughput.
- Safety\-by\-Design AI Governance — Comfortable operating within AI governance expectations, including risk assessment, sensitive data handling, model output validation, and auditability, and proactively identifying where AI use is not appropriate or requires additional controls.
- ML/AI \& Systems Domain Knowledge — Working understanding of Machine Learning, GenAI, LLM serving and inference, AI Agent architectures, DevOps, and systems engineering principles; sufficient to engage credibly with technical teams, drive meaningful tradeoffs, and identify cross\-system risks.
- Strategic Vision \& Entrepreneurial Leadership — Strong ability to analyze organizational context, anticipate future scenarios, and shape program roadmaps aligned to company\-level strategy and OKRs, with an entrepreneurial spirit that thrives in ambiguity and builds programs from the ground up.
- Executive Influence \& Thought Leadership — Demonstrated success influencing senior and executive leadership, representing program interests at the highest levels, providing well\-reasoned input to critical decisions, and building trusted relationships across disciplines and organizations without relying on formal authority.
- Analytical Problem\-Solving \& Risk Mitigation — Strong analytical and decision\-making skills with a track record of using data, innovative approaches, and structured frameworks to navigate complexity, resolve ambiguity, and mitigate program risks proactively.
- Operational Excellence at Scale — Demonstrated experience creating and driving efficient, scalable processes and best practices that increase organizational agility, with a focus on institutionalizing repeatable mechanisms that outlast individual programs and enable teams to execute with greater consistency.
- Exceptional Communication \& Collaboration — Outstanding written, verbal, and interpersonal communication skills; able to distill nuanced, technical concepts for diverse audiences, build consensus across competing perspectives, and foster an inclusive, collaborative environment that reflects Pinterest's commitment to diversity and belonging.
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.
Salary Context
This $145K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 25% above the category median. Disclosed range: $145K to $300K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Pinterest AI Hiring
Pinterest has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span San Francisco, CA, US, Remote, US. Compensation range: $209K - $550K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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