[All remote jobs](https://jobicy.com/jobs.md)[![Pinterest logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/01/86709a608bd914b28221164e6680ebf7.jpeg)](https://jobicy.com/company/pinterest.md)[Pinterest](https://jobicy.com/company/pinterest.md) # Machine Learning Engineer, Core Engineering Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/pinterest.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 138,905–285,982 / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceOpen levelPublished28 Oct 2026Apply before27 Nov 2026Listing views48Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Pinterest is hiring a Machine Learning Engineer for Core Engineering to develop large-scale personalization and recommendation technology. The role focuses on deep-learning models, candidate retrieval, data pipelines, and experimentation across product areas such as Homefeed, Ads, Growth, Shopping, and Search. Candidates need at least two years of applied ML experience plus hands-on work with distributed data and ML systems, including Hadoop or Spark. This is a high-impact engineering role with frequent experimentation and launches, requiring close collaboration across product and engineering teams. The position is remote for US-based applicants and includes an explicitly disclosed base-salary range plus equity eligibility. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThe position requires production-grade machine learning expertise across recommender systems, large-scale data processing, and experimentation. Success depends on translating advanced modeling work into measurable product improvements in a fast-moving environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$212,443.50US market range (per year)$150k–$260k0$286k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base salary range of $138,905–$285,982 USD. The yearly cadence is inferred from the stated full-time US professional base-pay range, as no explicit pay period is provided. A reasonable estimated US market range for a machine learning engineer working on large-scale recommendation systems is $150,000–$260,000 per year; actual offers vary by location, level, and specialized experience. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Deep Learning](https://jobicy.com/jobs?search_keywords=Deep%20Learning.md)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Candidate Retrieval](https://jobicy.com/jobs?search_keywords=Candidate%20Retrieval.md)[Ranking Models](https://jobicy.com/jobs?search_keywords=Ranking%20Models.md)[Data Pipelines](https://jobicy.com/jobs?search_keywords=Data%20Pipelines.md)[Apache Spark](https://jobicy.com/jobs?search_keywords=Apache%20Spark.md)[Hadoop](https://jobicy.com/jobs?search_keywords=Hadoop.md)[Large-Scale ML Systems](https://jobicy.com/jobs?search_keywords=Large-Scale%20ML%20Systems.md)[Experimentation](https://jobicy.com/jobs?search_keywords=Experimentation.md)[Natural Language Processing](https://jobicy.com/jobs?search_keywords=Natural%20Language%20Processing.md)[Graph Representation Learning](https://jobicy.com/jobs?search_keywords=Graph%20Representation%20Learning.md)[Real-Time Stream Processing](https://jobicy.com/jobs?search_keywords=Real-Time%20Stream%20Processing.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md) Sample interview questionsHow would you improve candidate retrieval for a personalized recommendation surface?I would begin by defining the retrieval objective and offline metrics such as recall, coverage, freshness, and downstream ranking impact. I would analyze user and content signals, establish a baseline retrieval strategy, then test approaches such as embedding-based approximate nearest-neighbor retrieval, multi-interest user representations, and graph-derived candidates. I would validate changes through offline evaluation and a controlled online experiment with guardrails for latency and engagement quality. Describe how you would design a scalable training and inference pipeline for a recommendation model. I would separate reliable feature generation, training, validation, model registration, and serving into observable pipeline stages. Distributed processing tools such as Spark would prepare large event and feature datasets, while versioned datasets and model artifacts would support reproducibility. For serving, I would define latency budgets, monitor feature freshness and prediction drift, and use rollback mechanisms for safe deployment. How do you decide whether an ML experiment should launch? I would first establish a primary success metric, supporting metrics, and guardrail metrics before the experiment begins. I would check statistical validity, segment results for important user populations, investigate unexpected movement in guardrails, and assess operational effects such as latency or system cost. A launch decision should combine statistical evidence, product significance, and confidence that the system can operate reliably at scale. What challenges arise when moving a deep-learning model from offline evaluation to production? Common challenges include training-serving skew, delayed or missing features, data leakage, increased inference latency, distribution shift, and weak observability. I address these through shared feature definitions, point-in-time-correct training data, robust data validation, shadow or canary deployments, and monitoring for performance, drift, errors, and latency. I also ensure there is a tested rollback path before broad exposure. How would you collaborate with product and engineering partners on an ML-driven feature? I would align early on the user problem, target metric, constraints, and experiment design. I would communicate model trade-offs in product terms, such as relevance, diversity, latency, and infrastructure cost, while involving platform engineers on data and serving requirements. Throughout development, I would share progress, risks, and results clearly so the final decision is evidence-based and jointly owned. Opportunity details ## About this role. 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](https://www.pinterestcareers.com/our-life/pinflex/) 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](https://www.pinterestcareers.com/our-philosophy-on-ai-in-hiring/). With more than 500 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else. What you’ll do: * Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest * Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas * Use data driven methods and leverage the unique properties of our data to improve candidates retrieval * Work in a high-impact environment with quick experimentation and product launches * Keeping up with industry trends in recommendation systems What we’re looking for: * 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning) * End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark) * Bachelor’s degree in computer science, machine learning, statistics, a related field or equivalent experience * Nice to have: M.S. or PhD in Machine Learning or related areas * Publications at top ML conferences * Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring * Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration * Expertise in scalable realtime systems that process stream data * Passion for applied ML and the Pinterest product Relocation Statement: * This position is not eligible for relocation assistance. Visit our [PinFlex](https://www.pinterestcareers.com/pinflex/) page to learn more about our working model. #LI-SA1 #LI-REMOTE 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](https://www.pinterestcareers.com/pinterest-life/). US based applicants only $138,905—$285,982 USD 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](https://forms.gle/sS24D4WboEKUZMQK6) 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. 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