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science, a related field or equivalent experience</li>\n<li>Experience building large-scale full-stack products</li>\n<li>Deep understanding of web development and best practices in React/Redux</li>\n<li>Strong experience with programming languages Javascript and Python/Java</li>\n<li>Strong software engineering principles and practices</li>\n<li>Strong collaboration and communication skills</li>\n<li>Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs</li>\n<li>Strong track record of critical evaluation and verification of AI-assisted work</li>\n</ul>","enriched_at":1781789331633},{"id":"job_74408567-d7a","title":"Software Engineer II, Big Data, tvScientific","source_url":"https://job-boards.greenhouse.io/pinterest/jobs/7782546","location":"San Francisco, CA, US; Remote, US","job_type":"full-time","experience_level":"mid","work_arrangement":"remote","category":"Engineering","description":"<p>We&#39;re looking for a Data Engineer to join our team at tvScientific._RAW</p>\n<p>As a Data Engineer at tvScientific, you will be a key player in implementing the robust data infrastructure to power our data-heavy company. You will collaborate with our cross-functional teams to evolve our core data pipelines, design for efficiency as we scale, and store data in optimal engines and formats.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and implement robust data infrastructure in AWS, using Spark with Scala</li>\n<li>Evolve our core data pipelines to efficiently scale for our massive growth</li>\n<li>Store data in optimal engines and formats, matching your designs to our performance needs and cost factors</li>\n<li>Collaborate with our cross-functional teams to design data solutions that meet business needs</li>\n<li>Design and implement knowledge graphs, exposing their functionality both via Batch Processing and APIs</li>\n<li>Leverage and optimize AWS resources while designing for scale</li>\n<li>Collaborate closely with our Data Science and Product teams</li>\n</ul>\n<p>How we&#39;ll define success:</p>\n<ul>\n<li>Successful design and implementation of scalable and efficient data infrastructure</li>\n<li>Timely delivery and optimization of data assets and APIs</li>\n<li>High attention to detail in implementation of automated data quality checks</li>\n<li>Effective collaboration with cross-functional teams</li>\n</ul>\n<p>What we&#39;re looking for:</p>\n<ul>\n<li>Production data engineering experience</li>\n<li>Proficiency in Spark and Scala, with proven experience building data infrastructure in Spark using Scala is preferred</li>\n<li>Experience in delivering significant technical initiatives and building reliable, large scale services</li>\n<li>Experience in delivering APIs backed by relationship-heavy datasets</li>\n<li>Familiarity with data lakes, cloud warehouses, and storage formats</li>\n<li>Strong proficiency in AWS services</li>\n<li>Expertise in SQL for data manipulation and extraction</li>\n<li>Excellent written and verbal communication skills</li>\n<li>Bachelor&#39;s degree in Computer Science or a related field</li>\n<li>Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs</li>\n<li>Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)</li>\n<li>High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables</li>\n</ul>\n<p>Nice-to-haves:</p>\n<ul>\n<li>Experience in adtech</li>\n<li>Experience implementing data governance practices, including data quality, metadata management, and access controls</li>\n<li>Strong understanding of privacy-by-design principles and handling of sensitive or regulated data</li>\n<li>Familiarity with data table formats like Apache Iceberg, Delta</li>\n<li>Previous experience building out a Data Engineering function</li>\n<li>Proven experience working closely with Data Science teams on machine learning pipelines</li>\n</ul>\n<p>In-Office Requirement Statement:</p>\n<ul>\n<li>We recognize that the ideal environment for work is situational and may differ across departments.</li>\n</ul>\n<p>Relocation Statement:</p>\n<ul>\n<li>This position is not eligible for relocation assistance.</li>\n</ul>\n<p>At tvScientific, 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.</p>\n<p>US based applicants only</p>\n<p>$123,696-$254,667 USD</p>\n<p>Our Commitment to Inclusion:</p>\n<p>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.</p>","enriched_at":1780024033803},{"id":"job_1944a239-03f","title":"Group Product Manager II, Creative Tech - tvScientific","source_url":"https://job-boards.greenhouse.io/pinterest/jobs/7890195","location":"San Francisco, CA, US; Remote, US","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"<p>At tvScientific, we&#39;re looking for a Group Product Manager II to lead product development for our CTV creative tooling. As a key member of our Creative Tech team, you&#39;ll be responsible for making creative the next pillar of optimization at tvSci, alongside bidding, targeting, measurement, and identity.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Lead product vision, strategy, and execution for tvSci&#39;s CTV creative tooling, from asset ingestion through generation, variant production, and creative-aware optimization</li>\n<li>Develop new tools that turn existing brand assets into TV-ready commercials, and generate creative variants and new concepts from those assets</li>\n<li>Expand tvSci&#39;s prediction and optimization models to incorporate creative as a core performance lever</li>\n<li>Partner with commercial teams and operations to make creative optimization a primary driver of customer performance, and ensure it&#39;s well understood and adopted by advertisers and internal teams</li>\n<li>Use AI to accelerate prototyping, explore creative concepts, and synthesize customer feedback, while applying judgment and verification to ensure correctness and quality</li>\n<li>Represent tvSci externally as a thought leader at the frontier of AI-driven creative technology</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>8+ years of product management experience, with expertise in creative technology, dynamic creative optimization, or generative AI for video and image</li>\n<li>Experience working with optimization and ML systems, and a clear point of view on how creative signal should be incorporated alongside bidding and targeting</li>\n<li>AI-forward and experimental, comfortable building with generative AI as a core primitive rather than a feature</li>\n<li>Demonstrated track record shipping creative technology products or optimizing ads at scale, with concrete examples of impact</li>\n<li>Excellent communication and stakeholder alignment skills, with comfort presenting to senior executives, customers, and industry audiences</li>\n<li>Demonstrated experience using AI to accelerate product development and customer analysis, with a clear approach to validating accuracy and quality</li>\n<li>High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables</li>\n<li>Bachelor&#39;s degree in a relevant field such as computer science, engineering, or a related discipline, or equivalent experience</li>\n</ul>\n<p>Relocation Statement: This position is not eligible for relocation assistance.</p>\n<p>In-Office Requirement Statement: 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.</p>\n<p>Benefits:</p>\n<ul>\n<li>Base salary range: $195,738 - $402,990 USD</li>\n<li>Eligible for equity</li>\n<li>Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise</li>\n<li>Information regarding the culture at tvScientific and benefits available for this position can be found here.</li>\n</ul>","enriched_at":1778875662485},{"id":"job_98858623-456","title":"ML Platform Engineer, tvScientific","source_url":"https://job-boards.greenhouse.io/pinterest/jobs/7782571","location":"San Francisco, CA, US; Remote, US","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"<p>We&#39;re looking for an ambitious Systems / Platform Engineer to join a team at the intersection of SRE and low-latency distributed systems. This team will help power Pinterest&#39;s next generation of realtime ML and measurement infrastructure, with a focus on sub-millisecond decisioning, high-throughput data access, and tight integration with Pinterest&#39;s core tech stack.</p>\n<p>In this role, you&#39;ll think about queries and RPCs in terms of syscalls, cache lines, and wire formats, and design systems that stay fast and predictable under load. You&#39;ll help define and harden the foundation for our training and serving stack: from storage and indexing strategies, to streaming and fanout, to backpressure and failure handling across services and regions.</p>\n<p>You&#39;ll work closely with software engineering, data infra, and SRE partners to ensure our systems are observable, debuggable, and operable in production. If topics like IO scheduling and batching, lock-free or low-contention data structures, connection pooling, query planning, kernel and network tuning, on-disk layout and indexing, circuit-breaking, autoscaling, incident response, NixOS, Rust, and robust SLIs/SLOs sound interesting (even if it&#39;s just a subset), this role gives you a chance to apply that expertise to business-critical, high-leverage infrastructure at Pinterest scale.</p>\n<p>What you&#39;ll do:</p>\n<ul>\n<li>Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments</li>\n</ul>\n<ul>\n<li>Improve the developer experience for the data science team</li>\n</ul>\n<ul>\n<li>Upgrade our observability tooling</li>\n</ul>\n<ul>\n<li>Make every deployment smooth as our infrastructure evolves</li>\n</ul>\n<p>What we&#39;re looking for:</p>\n<ul>\n<li>Deep understanding of Linux</li>\n</ul>\n<ul>\n<li>Excellent writing skills</li>\n</ul>\n<ul>\n<li>A systems-oriented mindset</li>\n</ul>\n<ul>\n<li>Experience in high-performance software (RTB, HFT, etc.)</li>\n</ul>\n<ul>\n<li>Software engineering experience + reliability (e.g. CI/CD) expertise</li>\n</ul>\n<ul>\n<li>Strong observability instincts</li>\n</ul>\n<ul>\n<li>Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs</li>\n</ul>\n<ul>\n<li>Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)</li>\n</ul>\n<ul>\n<li>High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables</li>\n</ul>\n<p>Nice-To-Haves:</p>\n<ul>\n<li>Reverse-engineering experience</li>\n</ul>\n<ul>\n<li>Terraform, EKS, or MLOps experience</li>\n</ul>\n<ul>\n<li>Python, Scala, or Zig experience</li>\n</ul>\n<ul>\n<li>NixOS experience</li>\n</ul>\n<ul>\n<li>Adtech or CTV experience</li>\n</ul>\n<ul>\n<li>Experience deploying a distributed system across multiple clouds</li>\n</ul>\n<ul>\n<li>Experience in hard real-time low-latency</li>\n</ul>","enriched_at":1777032830411}],"category_normalised":[{"category":"engineering","count":13},{"category":"sales","count":1}],"velocity":{"weeks":[{"week_start":"2026-04-13","count":3},{"week_start":"2026-04-20","count":2},{"week_start":"2026-05-11","count":1},{"week_start":"2026-05-25","count":1},{"week_start":"2026-06-15","count":7}],"trend":"stable","wow_pct":600},"momentum":{"recent_14d":7,"prior_14d":1,"growth_pct":600,"classification":"stable"},"salary_vs_industry":{"company_median":189181.5,"industry_median":null,"percentile":null,"sample_size":11,"by_region":[{"region":"United States","company_median":189181.5,"industry_median":null,"sample":11}],"transparency_pct":79,"industry_transparency_pct":0,"transparency_warning":false},"market_share":{"company_jobs":14,"industry_total":14,"share_pct":100,"rank":1,"peer_count":1},"ai_exposure":{"occupation_weighted_score":0.298,"skill_weighted_score":0.317,"top_exposed_titles":[],"top_exposed_skills":[{"skill":"Python","count":4,"score":0.331},{"skill":"Apache Spark","count":4,"score":0.331},{"skill":"Scala","count":4,"score":0.331},{"skill":"AWS","count":3,"score":0.288},{"skill":"Data Engineering","count":3,"score":0.288}]},"peer_set":[],"skills_lq":[],"geographic_shift":{"current":[{"region":"United States","count":14,"share_pct":100}],"emerging":[{"region":"United States","recent_30d":8,"prior_30d":3,"growth_pct":167}],"shrinking":[]},"seniority_anomalies":{"exec_recent_30d":0,"exec_prior_90d_avg":0,"exec_growth_pct":0,"notable_exec_hires":[]},"posting_dynamics":{"median_days_open":20,"industry_median_days_open":null,"long_open_count":0,"closure_rate_pct":30}}}}