The RoleDigital Turbine is transforming from a distribution company into a data-intelligence company. At the center of that shift is our ML platform and the "Next Best Action" framework - the capability that lets every DT product activate user-level models in real time against our unified, first-party device-level data (the DT User Card).
As a Senior Machine Learning Engineer, you'll design and scale the ML systems behind ad ranking, bid optimization, recommendation, and user-level prediction across our demand and supply businesses - the DSP, offer wall, content media, notifications, and Ignite. You'll build the pipelines and serving infrastructure that turn our data advantage into measurable lifts in CTR, CVR, and ROI, and help DT show up as an AI-first company.
What you'll do- Design, build, and deploy large-scale ML systems for ad ranking, click-through and conversion prediction, bid optimization, and next-best-action recommendation.
- Build and operate production ML pipelines - feature engineering, distributed training, deployment, retraining, and monitoring - that process billions of events and serve predictions at low latency.
- Leverage the data assets from across the DT product portfolio to enable ML solutions that create decisioning in real time using the entire breadth of available features.
- Optimize training and inference for compute efficiency (CPU/GPU utilization), latency, and cost across distributed systems.
- Partner closely with Data Science, Data Engineering, and product teams to ship models that move key business metrics, and run rigorous online experiments to validate impact.
- Stay current with applied ML research and bring innovative techniques to advertising, ranking, and audience modeling.
What you'll bring- 8+ years building and deploying ML systems in production at scale, ideally in AdTech, recommender systems, search/ranking, or real-time bidding.
- Strong software-engineering fundamentals - data structures, algorithms, system design - and proficiency in Python (plus Java, Scala, or C++ a strong plus).
- Hands-on experience with modern ML frameworks (PyTorch, TensorFlow, or JAX) and distributed data/compute systems (Spark, Beam/Dataflow, Flink, or similar).
- Experience building robust ML pipelines: feature stores, data versioning, model monitoring, and automated retraining.
- A record of shipping models that demonstrably improved product or business metrics (e.g., CTR, CVR, ROAS).
- Master's or Ph.D. in CS, ML, or a related field, or equivalent practical experience.
Nice to have- Experience with real-time, low-latency model serving and online learning.
- Familiarity with Databricks / Unity Catalog, semantic layers, or feature platforms.
- Experience with LLMs and agentic systems applied to audience insight or optimization.
