Software Engineer II, ML Ops, tvScientific

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

In this role, you’ll work at the intersection of SRE and low-latency distributed systems, with plenty of room to go deep on complex technical problems. You’ll help build and operate the platform that powers AI model training, deployment and serving, contributing to meaningful, production-facing projects from day one.

You’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’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. You’ll work closely with software engineering, data infra and SRE partners to ensure our systems are observable, debuggable and operable in production. You’ll interact with IO scheduling and batching, lock-free and 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.

What you’ll do

• Contribute to the infrastructure supporting AI workflows — training pipelines, Kubernetes deployments and CI/CD.
• Help improve the developer experience for the data science team — small frictions add up, and you’ll help eliminate them.
• Build out and improve observability tooling — learning to see the system clearly is a core skill we’ll develop together.
• Keep deployments clean and correct as the platform evolves.
• Grow into a deeper technical contributor under the mentorship of senior engineers who have done this at high scale.

What we’re looking for

• A genuine, demonstrable depth in Linux — hands-on experience beyond basic usage (for example, debugging, configuration or performance tuning).
• Strong software engineering fundamentals — you write clean code, reason about systems and debug methodically.
• A systems-oriented mindset — you think about why things work, not just that they work.
• Early exposure to reliability concepts — CI/CD, infrastructure-as-code or similar.
• An ownership mindset — especially when diagnosing and resolving production or project issues.
• Comfort using AI tooling to accelerate your work, with the discipline to verify what it produces.
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
• A track record of critically evaluating and validating AI-assisted work (for example, 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.
• 2+ years of experience building and operating high-performance distributed systems.
• Bachelor’s degree in computer science, engineering, a related field or equivalent experience.
• Nice-to-haves
  • Experience with NixOS or other tools for reproducible builds, and an interest in making development environments predictable and reliable.
  • Experience with Zig or similar low-level languages, and curiosity about what your compiler and runtime are doing under the hood.
  • You’ve reverse-engineered something — a protocol, a binary, a game, etc.
  • You’ve deployed something real to Kubernetes, even if it was a homelab.
  • Experience with Terraform or other infrastructure-as-code tools in a real context.
  • Exposure to adtech, CTV or other high-performance/low-latency environments.
  • Python or Scala experience in a data-adjacent context.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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