{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/title/ml-platform-engineer-tvscientific"},"x-facet":{"type":"title","slug":"ml-platform-engineer-tvscientific","display":"ML Platform Engineer, tvScientific","count":1},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_98858623-456"},"title":"ML Platform Engineer, tvScientific","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>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_98858623-456","directApply":true,"hiringOrganization":{"@type":"Organization","name":"tvScientific","sameAs":"https://www.tvscientific.com/","logo":"https://logos.yubhub.co/tvscientific.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/7782571","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$123,696-$254,667 USD","x-skills-required":["Linux","high-performance software","software engineering","reliability","observability","AI","data structures","kernel and network tuning","on-disk layout and indexing","circuit-breaking","autoscaling","incident response","NixOS","Rust"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:13:50.411Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, US; Remote, US"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Linux, high-performance software, software engineering, reliability, observability, AI, data structures, kernel and network tuning, on-disk layout and indexing, circuit-breaking, autoscaling, incident response, NixOS, Rust","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":123696,"maxValue":254667,"unitText":"YEAR"}}}]}