ML Platform Engineer

tvScientific • San Francisco, CA

11h • Remote

About The Position

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers, leveraging massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Their solution combines media buying, optimization, measurement, and attribution in one efficient platform, built by industry leaders in programmatic advertising, digital media, and ad verification. The company is seeking 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’s next generation of realtime ML and measurement infrastructure, focusing on sub‑millisecond decisioning, high‑throughput data access, and tight integration with Pinterest’s core tech stack. In this role, you will think about queries and RPCs in terms of syscalls, cache lines, and wire formats, and design systems that remain fast and predictable under load. You will help define and harden the foundation for the training and serving stack, encompassing storage and indexing strategies, streaming and fanout, and backpressure and failure handling across services and regions. You will work closely with software engineering, data infra, and SRE partners to ensure systems are observable, debuggable, and operable in production. This role offers a chance to apply expertise in areas such as 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 to business‑critical, high‑leverage infrastructure at Pinterest scale.

Requirements

• Deep understanding of Linux
• Excellent writing skills
• A systems-oriented mindset
• Experience in high-performance software (RTB, HFT, etc.)
• Software engineering experience + reliability (e.g. CI/CD) expertise
• Strong observability instincts
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

Nice To Haves

• Reverse-engineering experience
• Terraform, EKS, or MLOps experience
• Python, Scala, or Zig experience
• NixOS experience
• Adtech or CTV experience
• Experience deploying a distributed system across multiple clouds
• Experience in hard real-time low-latency (<10 ms) environments

Responsibilities

• Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
• Improve the developer experience for the data science team
• Upgrade our observability tooling
• Make every deployment smooth as our infrastructure evolves.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed