Sr. Machine Learning Engineer

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.

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 What we're looking for 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 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 Additional Information: Zone A: $178,700 - $232,300 Zone B: $159,100 - $206,800 Zone C: $140,700 - $182,900 This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate’s relevant experience, professional background,