Sr. Machine Learning Engineer

Unity Technologies SF; Mountain View (CA); On-site; USD 141,000 - 232,000; Full time

Benefits offered by this job

Commute subsidy

Employee stock ownership

Training and development programs

Health, life, and disability insurance

Job summary

Unity Technologies SF is seeking a Sr. Machine Learning Engineer to build and operate the ML infrastructure powering product intelligence and production ML models. This role spans data, features, distributed training, orchestration, serving, and observability.

You will own end-to-end projects, collaborate across teams, and iterate on platform improvements while learning new tools. Office presence in Mountain View is expected with competitive salary and equity options.

Qualifications

• Experience building ML infrastructure, data platforms, or distributed systems in production.
• Strong Python skills and experience working with data-intensive workloads.
• Hands-on experience with ML frameworks (e.g., PyTorch) and distributed compute (e.g., Ray, Spark).
• Experience with data pipelines, model training workflows, or large datasets beyond academic settings.
• Comfort working across multiple layers of the ML stack, with the ability to learn 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.

Responsibilities

• Build and maintain data and feature pipelines that produce training datasets for machine learning models and experimentation.
• Develop and improve infrastructure that supports distributed training workflows.
• Design, build, and operate multi-stage ML pipelines using workflow orchestration tools.
• Contribute to model serving and deployment infrastructure, supporting reliable movement of models from training into production.
• Enhance reproducibility and reliability across the stack using monitoring, alerting, dataset validation, and testing.
• Own end-to-end projects scoped with a manager, carrying work 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 long-term platform direction, delivering on immediate needs while progressing the platform.
• Partner with ML engineers, researchers, and peers across teams, sharing progress and technical tradeoffs with managers and directors.
• Adapt as team priorities shift by learning new tools and new parts of the ML stack.

Skills

Python

PyTorch

Ray

Spark

Education

Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field, or equivalent practical experience

Tools

Kubernetes

GCP

AWS

Prometheus

Grafana

Airflow

Prefect

Triton

KServe

Ray Serve

Kafka

Job description

Unity’s ML Engineering Infrastructure team is seeking a Sr. Machine Learning Engineer to help build and operate the platform that powers product intelligence and production machine learning models. This role covers the full ML infrastructure lifecycle, from data and features through distributed training, orchestration, serving, and observability.

Responsibilities

• Build and maintain data and feature pipelines that produce training datasets for machine learning models and experimentation.
• Develop and improve infrastructure that supports distributed training workflows.
• Design, build, and operate multi-stage ML pipelines using workflow orchestration tools.
• Contribute to model serving and deployment infrastructure, supporting reliable movement of models from training into production.
• Enhance reproducibility and reliability across the stack using monitoring, alerting, dataset validation, and testing.
• Own end-to-end projects scoped with a manager, carrying work 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 long-term platform direction, delivering on immediate needs while progressing the platform.
• Partner with ML engineers, researchers, and peers across teams, sharing progress and technical tradeoffs with managers and directors.
• Adapt as team priorities shift by learning new tools and new parts of the ML stack.

Requirements

• Experience building ML infrastructure, data platforms, or distributed systems in production.
• Strong Python skills and experience working with data-intensive workloads.
• Hands-on experience with ML frameworks (e.g., PyTorch) and distributed compute (e.g., Ray, Spark).
• Experience with data pipelines, model training workflows, or large datasets beyond academic settings.
• Comfort working across multiple layers of the ML stack, with the ability to learn 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.

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

Compensation and Location

Location: Mountain View, CA (onsite).
Base salary: USD 140,700 - 232,300 per year.

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

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

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.