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About Vibe Streaming TV is no longer just a brand awareness channel. It’s becoming a core part of the performance marketing mix, alongside search and social. co is building the platform that makes TV work like the rest of the acquisition stack: targeted, measurable, and optimized for results. co is an Audience First Streaming TV Advertising platform that gives marketers the tools to reach the right audiences, optimize campaigns in real time, and understand what’s actually driving business outcomes. With hyper-targeted segmentation, AI-powered recommendations, real-time optimization, and incrementality measurement, Vibe brings the precision and accountability of digital advertising to the biggest screen in the house. co to reach 120 million+ households. co became a Walmart company, opening up a new chapter for the business and the industry. With Walmart’s first-party data and purchase signals now part of the Vibe platform, advertisers can connect TV advertising to real-world transactions and measure the impact of CTV with a level of precision previously associated with platforms like Google and Meta. This is a unique moment to join Vibe. With Walmart’s scale, data, and reach behind us, we’re building the next generation of TV advertising and creating a more measurable, performance-driven future for the channel. About the role The Performance team owns advertiser outcomes at Vibe: the prediction models, the bidding engine, pacing, and the delivery stack underneath them. You'd report to Clément Schiano, Engineering Manager for Performance. We're hiring because our infrastructure work has become two jobs. One is building the platform and tooling that gets data scientists from experiment to production quickly. The other is keeping the production serving path fast, cheap and reliable as traffic grows. One person currently does both, and the reliability half loses whenever there's a modeling deadline. This role takes over that half. Why it's interesting: Scale is the problem we have. Vibe was acquired by Walmart and the plan is to run many more advertisers through the same bidding stack. How much of that we can take on depends directly on what the infrastructure can absorb. Every bid is a real-time decision with advertiser budget behind it. If p99 moves the wrong way, advertisers under-deliver and we lose money that day. Cost works the same way: replica count and cloud bill are things you can move. The team is seven people, so you'd have room to set direction on reliability. The stack already exists and works, so you're not starting from a blank page. What You'll Do Own reliability across the production stack: prediction serving, the bidding engine, pacing, delivery. Define the SLOs, build the observability to see them, and shape how we run on-call. Bring p99 down on the serving path and run the bidding engine on fewer replicas without giving up latency. Work on the data layer under real-time serving. Redis comes first: access patterns, caching strategy, behavior when it degrades. Build a way to stress-test the platform end to end in staging so we find our ceiling before production does. Handle capacity planning and infrastructure cost as advertiser volume grows. Work closely with the data scientists shipping models into this stack, and with Benoît Delsart, who covers the platform and tooling side. Run incident response and make sure postmortems turn into changes that ship. Collaborate with other engineering teams, notably DSP, since our systems interact heavily with the rest of the stack. What You Need You've owned latency-sensitive services in production, on-call included. You've been paged for something serious and know how that goes. Solid Kubernetes and cloud fundamentals. At ease debugging a p99 latency problem Observability experience: metrics, tracing, logs, and opinions about what makes an alert worth waking someone for. Infrastructure as code, CI/CD.

Senior SRE/ML Ops Engineer - Performance

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