Sr. Machine Learning Engineer

Unity Technologies SF•Mountain View, CA

About The Position

Unity's ML Engineering Infrastructure team builds the platform that powers machine learning across the company, from feature generation and training data pipelines to distributed training, experimentation, and model serving. We're looking for a Machine Learning Engineer to help build the platform behind our product intelligence, production ML models, and the teams that develop them. Operating at scale across batch and streaming data, our systems power large-scale training, feature generation, and experimentation. You'll work across the full ML infrastructure stack, from data and feature pipelines to distributed training, orchestration, serving, and observability, going wherever the team needs you most. This role suits someone curious and eager to learn who picks up new tools quickly. Working closely with your manager and experienced engineers, you'll take on well-scoped projects and deliver them end-to-end using your own judgment, keeping both the day-to-day details and the broader direction of the platform in view.

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
Comfortable working across several layers of the ML stack, with a track record of learning new tools and systems quickly
Ability to spot 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
Sufficient knowledge of English to have professional verbal and written exchanges in this language

Nice To Haves

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

Responsibilities

Build and maintain data and feature pipelines that generate training datasets for machine learning 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, helping models move reliably from training into production
Improve reproducibility and reliability across the stack through monitoring, alerting, dataset validation, and testing
Take on projects scoped with your manager and carry them through end-to-end, from design to rollout, using your own judgment along the way
Investigate uncommon or inconsistent issues across pipelines and training systems, recognize the patterns behind them, and turn them into concrete improvements
Keep both the hands-on details and the bigger picture in mind, delivering on immediate needs while moving the platform in the right direction
Partner with ML engineers, researchers, and peers across teams, and share progress and tradeoffs with managers and directors
Pick up new tools and new parts of the stack as team priorities shift