Digital Turbine seeks a Principal Software Engineer to lead enterprise-wide ML and AI architecture. Responsibilities include defining long-term ML platform vision, solving complex technical challenges in model scaling and real-time inference, evaluating ML technologies, establishing MLOps standards, mentoring senior engineers, leading architecture governance, and fostering engineering culture. The role involves architecting large-scale distributed training and inference systems, integrating generative AI and LLM technologies, designing observability systems, and collaborating with executive leadership and cross-functional teams. Requirements include a Bachelor’s degree in a quantitative field (Master’s or Ph.D. preferred), 10+ years of software/ML engineering experience (or 8+ with advanced degree), expertise in PyTorch, TensorFlow, Jax, Kubernetes, cloud-native infrastructure, Python/C++/Rust programming, and leadership skills in strategic alignment and architectural governance. Location is hybrid in New York, USA or Berlin, Germany. Candidates must be local to the posting location.
What you'll do
Define the 2–3 year vision for ML architecture and build foundational platform capabilities
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Requirements
Bachelor’s degree in Computer Science, Machine Learning, Data Science, or related quantitative field (Master’s or Ph.D. preferred)
10+ years of software/ML engineering experience with a Bachelor’s degree, OR 8+ years with an advanced degree
Proven track record operating as a Principal (P5) or Staff (P4) level engineer on enterprise-scale systems
Expert-level mastery of PyTorch, TensorFlow, Jax, and modern distributed ML training frameworks
Comprehensive mastery of cloud-native infrastructure, container orchestration (Kubernetes), feature stores, CI/CD pipelines, and enterprise MLOps suites
Exceptional proficiency in system architecture, distributed computing, memory optimization, data structures, and languages such as Python, C++, or Rust
Demonstrated ability to drive strategic alignment, architectural consensus, and engineering compliance across non-reporting teams and business units
Ability to balance immediate execution needs with long-term architectural stability, scalability, and cost efficiency
