Principal Software Engineer

Digital Turbine•Berlin, NY

1d•Hybrid

About The Position

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. We are seeking a Principal Software Engineer to serve as an enterprise-wide technical authority and strategic architect for our Machine Learning and Artificial Intelligence ecosystem. Operating at the highest tier of individual contributor leadership, you will define the 2–3 year vision for our ML architecture, build foundation platform capabilities, and solve complex, novel technical problems that directly enable company-wide strategic objectives. In this role, you will work with a high degree of autonomy, serving as a company-wide subject matter expert. You will set engineering standards, drive architectural governance, and multiply the impact of the entire engineering organization by mentoring senior and staff engineers without direct administrative management responsibilities.

Requirements

Bachelor’s degree in Computer Science, Machine Learning, Data Science, or a related quantitative field (Master’s or Ph.D. preferred).
10+ years of software/ML engineering experience with a Bachelor’s degree, OR 8+ years of experience with an advanced degree (Master’s / Ph.D.), including a proven track record operating as a Principal (P5) or Staff (P4) level engineer on enterprise-scale systems.
Proven track record of architecting, deploying, and maintaining high-throughput, low-latency, mission-critical ML models and pipelines in real-time production environments.
Expert-level mastery of PyTorch, TensorFlow, Jax, and modern distributed ML training frameworks (e.g., DeepSpeed, Megatron-LM, Ray).
Comprehensive mastery of cloud-native infrastructure, container orchestration (Kubernetes), feature stores, CI/CD pipelines, and enterprise MLOps suites (AWS SageMaker, GCP Vertex AI, MLflow, Kubeflow).
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.

Nice To Haves

Demonstrated expertise in large-scale Generative AI architectures, foundation model pre-training/fine-tuning, guardrailing, agentic workflows, and high-performance vector retrieval systems.
Deep knowledge of distributed data processing engines (Ray, Apache Spark, Dask) and high-performance database design (Vector DBs, NoSQL, distributed caching).
Experience with specialized ML accelerators (GPU clusters, TPUs, custom silicon), CUDA programming, and hardware-level inference optimization (TensorRT, ONNX Runtime, vLLM).
Active open-source contributor, conference speaker, or author of peer-reviewed publications in machine learning or distributed systems.

Responsibilities

Define Enterprise Architecture: Lead the long-term vision, architectural direction, and multi-year technology roadmap for high-scale ML platforms, model deployment infrastructure, and generative AI systems across multiple product domains.
Complex Problem Solving: Solve novel, highly ambiguous, and precedent-setting engineering challenges in model scaling, distributed systems, real-time inference, and feature engineering.
Technology Evaluation & Build-vs-Buy: Evaluate frontier ML research, frameworks, hardware accelerators, and third-party vendor platforms to make strategic technology choices and build-vs-buy decisions for the enterprise.
Standardization & Governance: Establish company-wide MLOps best practices, security standards, evaluation frameworks, model governance protocols, and operational reliability metrics across all product engineering groups.
Mentorship & Talent Elevation: Act as a key mentor and technical sponsor for Staff (P4), Senior (P3), and peer engineers, elevating technical bar, design rigor, and execution speed across the organization.
Architecture Governance: Lead cross-departmental Architecture Review Boards (ARBs), approving critical system designs, data pipelines, and production deployment architectures.
Engineering Culture: Foster a culture of technical excellence, continuous learning, operational resilience, and principled engineering tradeoffs across ML and platform teams.
High-Scale Infrastructure: Architect and optimize large-scale distributed training clusters, feature platforms, high-throughput model serving engines, and cost-efficient inference pipelines.
GenAI & Frontier Tech: Lead the technical design and integration of advanced foundation models, LLM orchestration, retrieval-augmented generation (RAG), parameter-efficient fine-tuning (PEFT), and vector infrastructure.
System Resilience & Observability: Design enterprise-grade observability systems to monitor model drift, system health, data quality, security posture, and business metric impact at scale.
Strategic Alignment: Partner closely with VPs, Directors, Product Leaders, and Domain Experts to translate business vision into concrete, scalable technical roadmaps and architectural specs.
Technical Advocacy: Communicate complex technical concepts, trade-offs, risks, and strategic technical investments clearly to executive leadership and non-technical stakeholders.
Cross-Organizational Collaboration: Build strong operational bridges between ML Engineering, Data Engineering, Infrastructure/DevOps, Enterprise Security, and Product teams.