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.
Growth Content Science builds the ML that decides which content we bring to people on our growth surfaces, with a focus on notifications. We own the real-time production stack behind that decision: candidate retrieval, L1 and L2 ranking, utility and blending, content quality along with copy generation and ranking. As a Staff Machine Learning Engineer, you'll personally build the most complex models and systems on this stack, hold the bar for code quality and healthy ML infrastructure and lead the technical direction for notification content ranking.
What you'll do
- Lead multiple technical sub-areas within Growth content science effectively.
- Drive Growth ML model improvements through AB test driven innovation loop - from ideation to production launches.
- Own the health of our ML ecosystem: training pipelines, feature and serving infrastructure, evaluation frameworks and monitoring, AB test set up, etc.
- Help raise the technical excellence bar through mentorship, reviews, evaluations, documentation, standard setting, etc.
- Collaborate and build strong working relationships with colleagues and stakeholders to tackle big, cross-functional, initiatives.
- Work closely with other engineering leaders to form technical directions and roadmaps.
- Operate autonomously, take ownership in complex and evolving environments and drive work effectively.
- Identify and apply relevant state-of-the-art techniques from recent research and/or collaborate on novel research directions.
- Continuously optimize various parts of work by building and using cutting-edge AI tools.
What we're looking for
- Graduate degree in Computer Science or in a related field
- Minimum of 8 years of experience in machine learning engineering, including hands-on experience building and shipping models for a large-scale, real-time, end-user production recommender system
- Expertise in recommender modeling, including sequence modeling of user behavior with transformer architectures, DNN rankers and generative and LLM-native recommenders
- Experience in building and operating high-QPS, latency-constrained ML systems, including training and serving infrastructure, and in owning the cost, latency and reliability tradeoffs
- Experience in industry-grade software engineering that others use as the example, and in conducting thorough code and design reviews that raise the quality of a team's work
- Understanding of content personalization and content quality as one problem, and of why cold-start and low-signal users break standard rankers
- Experience in leading technical work across teams, making timely decisions on hard tradeoffs and saying no when the user experience or the system health is at stake
- Experience in mentoring junior engineers
- Outstanding written and verbal communication skills, especially in a cross-functional set up
- Demonstrated experience using AI to improve speed and quality in applied research
- Strong track record of publications and patents
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/quarter and therefore can be situated anywhere in the country.
Relocation Statement:
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.
Information regarding the culture at Pinterest and benefits available for this position can be found here.
Our Commitment to Inclusion:
Skills Required
- Graduate degree in Computer Science or a related field
- At least 8 years of machine learning engineering experience
- Hands-on experience building and shipping models for large-scale, real-time, end-user recommender systems
- Expertise in recommender modeling, including user behavior sequence modeling with transformer architectures, DNN rankers, and generative or LLM-native recommenders
- Experience building and operating high-QPS, latency-constrained machine learning systems
- Experience with ML training and serving infrastructure, including cost, latency, and reliability tradeoffs
- Industry-grade software engineering experience and thorough code and design review experience
- Understanding of content personalization, content quality, cold-start users, and low-signal users
- Experience leading technical work across teams and making decisions on complex tradeoffs
- Experience mentoring junior engineers
- Outstanding written and verbal communication skills in cross-functional environments
- Demonstrated experience using AI to improve speed and quality in applied research
- Strong track record of publications and patents
Pinterest Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Pinterest and has not been reviewed or approved by Pinterest.
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Fair & Transparent Compensation — Compensation is considered competitive in core technical roles, with clearly defined base, bonus, and RSU components. Vesting schedules and pay elements are articulated clearly, supporting visibility into total rewards.
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Parental & Family Support — Policies include substantial paid parental leave globally alongside fertility and family‑building benefits, adoption assistance, and dedicated caregiver supports. Communications also highlight a phased return‑to‑work approach.
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Flexible Benefits — PinFlex enables role‑dependent hybrid/remote work with home‑office, connectivity, and commuter support, plus the option to work internationally for a limited period with approval. Time away is reinforced by a paid company shutdown and generous vacation framing.
Pinterest Insights
What We Do
Pinterest is the visual inspiration platform people around the world use to shop products personalized to their taste, find ideas to do offline and discover the most inspiring creators. Today, more than 460 million people come to the platform every month to explore and experience billions of ideas that have been saved. We’re proud to help people to discover and do what they love.
