Sr. Machine Learning Engineer

Unity’s ML Engineering Infrastructure team builds and operates the platform behind product intelligence and production machine learning models. This onsite role in Mountain View, CA focuses on the end-to-end infrastructure stack, from data and feature pipelines to distributed training, orchestration, serving, and observability.

Along with working on high-impact ML systems, you’ll be supported by a benefits package designed for health, growth, and wellbeing, including comprehensive insurance coverage, generous time off, mental health programs, and training and development opportunities.

Responsibilities

• Build and maintain data and feature pipelines that produce training datasets for models and experimentation
• Develop and improve infrastructure for distributed training workflows
• Build and operate multi-stage ML pipelines using workflow orchestration tools
• Contribute to model serving and deployment infrastructure to move models reliably from training into production
• Improve reproducibility and reliability across the stack using monitoring, alerting, dataset validation, and testing
• Own end-to-end projects scoped with your manager, guiding work from design through rollout using sound 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 immediate needs while progressing the system
• Partner with ML engineers, researchers, and peers across teams, sharing progress and tradeoffs with managers and directors
• Adopt new tools and parts of the stack as team priorities evolve

Requirements

• 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, with a record of quickly learning new tools and systems
• Ability to detect 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

Technologies

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

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 such as Airflow or Prefect
• Experience with model serving frameworks such as Triton, KServe, or Ray Serve
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems such as Kafka)
• Familiarity with feature stores or experimentation platforms

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

Compensation and Additional Information

Salary: USD 140,700 - 232,300 per yearly.

• Zone A: $178,700 - $232,300
• Zone B: $159,100 - $206,800
• Zone C: $140,700 - $182,900

Beyond base salary, this role may be eligible for equity awards and participation in company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer will depend on geographic location and the candidate’s relevant experience, professional background, and skill set.