Sr. Software Engineer, ML Platform, Tvscientific

Pinterest Pinterest · Consumer · San Francisco, CA · tvScientific

ML Platform Engineer to join a team at the intersection of sysops, systems programming, architecture, and large-scale deployments. The platform underpins a real-time bidding agent and ML training system that drive $100M+ in annual revenue. Focus on building a Kubernetes + Ray backend for model training pipelines, and defining the next generation of the training and serving stack.

What you'd actually do

  1. Scale the decisionmaking process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
  2. Improve the developer experience for the data science team
  3. Upgrade our observability tooling
  4. Serve as a technical lead and mentor to the team
  5. Make every deployment smooth as our infrastructure evolves.

Skills

Required

  • 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 have

  • 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

What the JD emphasized

  • real-time bidding agent
  • ML training system
  • Kubernetes + Ray backend
  • training and serving stack
  • high-performance software (RTB, HFT, etc.)
  • critical evaluation and verification of AI-assisted work

Other signals

  • ML Platform Engineer
  • real-time bidding agent
  • ML training system
  • Kubernetes + Ray backend
  • training and serving stack