Job description

At Digital Turbine, we make mobile advertising experiences more meaningful and rewarding for users, app publishers, and advertisers — intelligently connecting people in more ways, across more devices. We provide app publishers and advertisers with powerful ads and experiences that captivate consumers, fuel performance, and help telecoms and OEMs supercharge awareness, acquisition, and monetization. In a rapidly evolving industry, we are constantly innovating and creating better paths of discovery to connect consumers, publishers, and advertisers across the mobile ecosystem. Please note that Digital Turbine is a hybrid work environment-only candidates local to the posting location will be considered. The Role Digital 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. About Digital Turbine: Digital Turbine (NASDAQ: APPS) powers superior mobile consumer experiences and results for the world’s leading telcos, advertisers and publishers. Our end-to-end platform uniquely simplifies the ability to supercharge awareness, acquisition and monetization — connecting our partners to more consumers, in more ways, across more devices. The company is headquartered in Austin, Texas, with global offices in New York, Los Angeles, San Francisco, London, Berlin, Singapore, Tel Aviv, and other cities around the world, serving top agency, app developer, and advertising markets. We are honored to have achieved numerous awards as an employer of choice, around the world, including: BuiltIn s Best Places to Work Awards in 2022, 2023 and 2024, DUNS 100 Best Places to Work in Tech for 2023 and 2024, and BDICode s 100 Best Companies to Work in 2024. Digital Turbine is an equal opportunity employer committed to exemplifying diversity and inclusion around the world. We welcome people of different backgrounds, experiences, abilities, and perspectives. We embed diversity in our mindset, products, and teams to empower an inclusive, equitable, and culturally fluent environment. Building and continuously fostering this culture within our teams makes us better collaborators, partners, and innovators. Digital Turbine will process the information you provide during the application process in accordance with the Digital Turbine Global Recruitment Privacy Notice .

Requirements

Must-haves

Please note that Digital Turbine is a hybrid work environment-only candidates local to the posting location will be considered.
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.
Familiarity with Databricks / Unity Catalog, semantic layers, or feature platforms.
Experience with LLMs and agentic systems applied to audience insight or optimization.

Nice-to-haves

Nice to have Experience with real-time, low-latency model serving and online learning.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningaipythonjava

Platform: workday
Effort: long