Digital Turbine seeks a Principal Software Engineer to lead enterprise-wide ML and AI architecture. The role involves defining a 2-3 year vision for ML platforms, solving complex technical challenges in model scaling and distributed systems, evaluating ML technologies, and establishing MLOps standards. Responsibilities include mentoring senior engineers, leading architecture governance, fostering engineering culture, architecting high-scale ML infrastructure, integrating generative AI and LLM technologies, designing observability systems, and collaborating with executive 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, Python, C++, Rust, and experience with cloud-native infrastructure and MLOps tools. Preferred qualifications include expertise in generative AI, distributed computing, hardware acceleration, and industry leadership. The position is hybrid and limited to candidates local to New York, NY or Berlin, Germany.
What you'll do
Define enterprise architecture and lead long-term vision and technology roadmap for high-scale ML platforms and generative AI systems
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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
Ability to balance immediate execution needs with long-term architectural stability, scalability, and cost efficiency
