Product EngineerDashreels

Why this role exists
Dashreels streams bite?sized, emotionally addictive dramas?70?minute series told through 12minute episodes?all born from generative?AI pipelines In a world where new episodes can be spun up hourly by creators and algorithms alike, discovery is just as much an ML problem as a storytelling one Were hiring product?minded engineers who can blend recommender?system science with shipping craft to surface the perfect episode, moment by moment, for millions of viewers Youll own the metrics?watch?time, completion, discovery depth?not just the systems that deliver them This is a full?stack role: youll invent Gen?AI?native ranking techniques, wrangle real?time data at scale, and polish every pixel of the end?user experience What you'll do Design, ship, and iterate full?stack recommendation features?front?end widgets, back?end services, and data pipelines?aimed squarely at boosting watch?time, completion, and discovery depth Launch to prod in week?1; improve every week Own the viewer metrics by shaping ranking services end?to?end?from candidate retrieval and feature stores to real?time scoring and edge delivery (Flutter + Kotlin) Invent Gen?AI?native ranking techniques?leverage multimodal embeddings, LLM?generated summaries, and agentic diversifiers to rank and diversify freshly generated episodes without historical signals Scale & optimise?build data pipelines that process billions of events, tune microservices for sub?50?ms latency, and keep infra costs predictable Instrument & iterate?build offline evaluation harnesses, run weekly A/Bs, and track hit?rate, completion, discovery depth, and content diversity You might thrive here if you? Are product-driven in ML: You evaluate every model change in terms of its impact on watch-time and retention Have a bias to ship: You'd rather launch an 80% accurate heuristic model today than keep a perfect one in a slide deck Have full-stack curiosity: You're just as comfortable tweaking a Flutter widget as you are optimizing a GNN feature Are experiment-obsessed: You get excited by p-values, guard-rail metrics, and exploring counterfactuals Are a story fan: You binge K-dramas, webtoons, or revenge romances?and you know exactly why you keep tapping "Next" We're excited about candidates who have 38years engineering experience, including at least one recommender or ranking system in production Fluency in Python and Java/Kotlin (serving) Exposure to A/B experimentation platforms and causal ML Bonus: experience with LLM?powered retrieval, agent?style recommenders (LangGraph, AutoGen, ReAct), or integrating embeddings from multimodal generative models (diffusion, TTS, image LDMs) into ranking pipelines Bonus: Some mobile engineering experience What's uniques about Dashreels Engineering AI?first catalogue Episodes are created natively through our Gen?AI studio, giving you metadata, scripts, and embeddings at day?zero Fast feedback loop Short content + high engagement = rich signals for hourly retrains Creator ? Viewer flywheel Your rankings boost watch?time and inform our studio which stories to green?light next