Sr. Machine Learning Engineer

Unity Technologies SF is building and operating an ML Engineering Infrastructure platform that helps teams move production machine learning reliably from training into deployed services. As a Senior Machine Learning Engineer, you will work across data and feature pipelines, distributed training, workflow orchestration, serving, and observability, partnering with ML engineers and researchers to improve end-to-end reliability.

What you’ll do

• Build and maintain data and feature pipelines that produce training datasets for model development and experimentation
• Develop and continuously improve infrastructure for distributed training workflows
• Create and run multi-stage ML pipelines using workflow orchestration tools
• Contribute to model serving and deployment infrastructure to support dependable transitions from training to production
• Strengthen reproducibility and reliability through monitoring, alerting, dataset validation, and testing across the stack
• Own scoped projects with your manager, carrying them from design through rollout using independent judgment
• Investigate uncommon or inconsistent issues across pipelines and training systems, identify underlying patterns, and implement concrete improvements
• Balance hands-on execution with broader platform direction, delivering on near-term needs while advancing the platform
• Partner with ML engineers, researchers, and teams across the organization, sharing progress and tradeoffs with managers and directors
• Adopt new tools and parts of the stack as team priorities evolve

Required skills and experience

• Experience building ML infrastructure, data platforms, or distributed systems in production
• Strong Python skills and experience with data-intensive workloads
• Hands-on experience with ML frameworks such as PyTorch and distributed compute such as Ray or Spark
• Experience with data pipelines, model training workflows, or large datasets beyond academic settings
• Comfort working across multiple layers of the ML stack and a record of learning new tools and systems quickly
• Ability to recognize patterns in ambiguous or inconsistent problems and translate them into practical technical needs
• Clear communication and a collaborative approach with peers, managers, and partner teams
• Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field, or equivalent practical experience

Tools you may work with

• Python, PyTorch, Ray, Spark, Kubernetes
• GCP, AWS
• Prometheus, Grafana
• Airflow, Prefect
• Triton, KServe, Ray Serve
• Kafka

Benefits

• 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

Nice to have

• Experience with Kubernetes and cloud infrastructure (GCP or AWS)
• Experience with observability tooling such as Prometheus and Grafana
• Experience with workflow orchestration systems like Airflow or Prefect
• Experience with model serving frameworks such as Triton, KServe, or Ray Serve
• Exposure to large-scale data platforms including data lakes, warehouses, and streaming systems such as Kafka
• Familiarity with feature stores or experimentation platforms

Location: Bellevue, WA (onsite). Salary range: USD 140,700 - 232,300 per year.