Principal Software Engineer

Digital Turbine

. Define the long-term vision, architectural direction, and multi-year technology roadmap for high-scale ML platforms, model deployment infrastructure, and generative AI systems .

Posted 10/7/2026full-timeNew York City • New York • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and optimizing high-scale ML platforms, model deployment infrastructure, and generative AI systems. Proficient in establishing MLOps best practices and leading cross-departmental collaborations to translate business vision into technical roadmaps.

Highest-signal resume keywords

Architecting High-Throughput ML ModelsExpertise in PyTorch and TensorFlowCloud-Native Infrastructure MasteryDistributed Systems OptimizationMLOps Best Practices Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringModel Deployment InfrastructureReal-Time InferenceFeature EngineeringSystem ArchitectureDistributed ComputingPython ProgrammingC++ ProgrammingRust ProgrammingData Structures

Soft Skills

Mentoring EngineersTechnical SponsorshipCross-Departmental CollaborationCommunication with StakeholdersStrategic Alignment

Tools & Technologies

KubernetesAWS SageMakerGCP Vertex AIMLflowKubeflowDeepSpeedMegatron-LMRayVector DBsNoSQL

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree in Machine LearningPh.D. in Data Science

Industry Keywords

Generative AIMLOpsModel GovernanceOperational ReliabilityHigh-Performance Vector Retrieval

Tech Stack

Tools & technologies

ApacheAWSCloudDistributed SystemsGoogle Cloud PlatformKubernetesNoSQLPythonPyTorchRayRustSparkTensorflowC++

About the role

Key responsibilities & impact

Define the long-term vision, architectural direction, and multi-year technology roadmap for high-scale ML platforms, model deployment infrastructure, and generative AI systems
Solve novel engineering challenges in model scaling, distributed systems, real-time inference, and feature engineering
Evaluate frontier ML research, frameworks, hardware accelerators, and third-party vendor platforms for build-vs-buy decisions
Establish company-wide MLOps best practices, security standards, evaluation frameworks, model governance protocols, and operational reliability metrics
Mentor and technically sponsor Staff, Senior, and peer engineers without direct administrative management responsibilities
Lead cross-departmental Architecture Review Boards and approve critical system designs, data pipelines, and production deployment architectures
Foster technical excellence, continuous learning, operational resilience, and principled engineering tradeoffs
Architect and optimize distributed training clusters, feature platforms, model serving engines, and inference pipelines
Lead design and integration of foundation models, LLM orchestration, RAG, PEFT, and vector infrastructure
Design observability systems for model drift, system health, data quality, security posture, and business metric impact
Partner with VPs, Directors, Product Leaders, and Domain Experts to translate business vision into technical roadmaps and architectural specifications
Communicate technical concepts, trade-offs, risks, and investments to executive leadership and non-technical stakeholders
Collaborate across ML Engineering, Data Engineering, Infrastructure/DevOps, Enterprise Security, and Product teams

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Machine Learning, Data Science, or a related quantitative field required; 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.)
Proven track record operating as a Principal (P5) or Staff (P4) level engineer on enterprise-scale systems
Proven track record 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 such as DeepSpeed, Megatron-LM, and Ray
Comprehensive mastery of cloud-native infrastructure, Kubernetes, feature stores, CI/CD pipelines, AWS SageMaker, GCP Vertex AI, MLflow, and Kubeflow
Exceptional proficiency in system architecture, distributed computing, memory optimization, data structures, and 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
Preferred: expertise in large-scale Generative AI architectures, foundation model pre-training/fine-tuning, guardrailing, agentic workflows, and high-performance vector retrieval systems
Preferred: knowledge of Ray, Apache Spark, Dask, Vector DBs, NoSQL, and distributed caching
Preferred: experience with GPU clusters, TPUs, custom silicon, CUDA, TensorRT, ONNX Runtime, and vLLM
Preferred: active open-source contribution, conference speaking, or peer-reviewed publications in machine learning or distributed systems

Benefits

Comp & perks

Hybrid work environment
Equal opportunity employer committed to diversity and inclusion
Inclusive, equitable, and culturally fluent environment