Senior/Staff Machine Learning Engineer, Data Infrastructure

Unity

Senior machine learning data infrastructure engineer building scalable training-data pipelines for Unity’s game engine and 3D platform. Improving distributed processing, orchestration, observability, and experimentation workflows.

Posted 8/13/2026full-timeMountain View • California, Washington • 🇺🇸 United StatesSenior💰 $200,400 - $260,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing large-scale data pipelines for machine learning, utilizing distributed computing frameworks like Flink, Spark, and Ray. Proficient in integrating orchestration systems and ensuring pipeline reliability and cost efficiency.

Highest-signal resume keywords

FlinkSparkRayPython ProgrammingData Pipeline Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed ComputingData Pipeline DevelopmentMachine Learning Feature EngineeringDataset ValidationAutomated Testing

Soft Skills

Systems ThinkingTechnical LeadershipInfluencing Architectural Decisions

Tools & Technologies

FlyteAirflowData LakesData WarehousesStreaming Platforms

Industry Keywords

Big Data ProcessingPerformance OptimizationResource UtilizationScalabilityReliability

Tech Stack

Tools & technologies

AirflowDistributed SystemsPythonRaySpark

About the role

Key responsibilities & impact

Develop infrastructure supporting batch and stream big data processing using Flink, Spark, Ray, and similar technologies
Design and operate large-scale data pipelines generating training datasets for machine learning training and experimentation
Integrate data pipelines with workflow orchestration systems such as Flyte and Airflow for reliable multi-stage training workflows
Improve pipeline reproducibility and observability through dataset validation, monitoring, and automated testing
Optimize performance and resource utilization across distributed compute systems
Partner with ML engineers to enable large-scale experimentation and model iteration
Lead architectural improvements to keep offline data pipelines scalable, reliable, and cost-efficient

Requirements

What you’ll need

Experience working with distributed computing frameworks such as Flink, Spark, and Ray for distributed data processing
Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines
Experience optimizing big data pipelines and infrastructure for cost efficiency
Strong programming skills in Python and experience with large-scale distributed workloads
Experience with modern data infrastructure, including data lakes, warehouses, orchestration systems, and streaming platforms
Strong systems thinking and ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systems
Proven ability to lead technical direction and influence architectural decisions across teams without formal authority
Sufficient knowledge of English for professional verbal and written exchanges
Work visa/immigration sponsorship is not available for this position

Benefits

Comp & perks

Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
Support for new parents through leave and family-care programs
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program
Equity awards
Participation in company incentive plans, such as annual discretionary bonuses or sales commissions