Job description

The opportunity 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. What you ll be doing 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, and skill set. Benefits At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance. Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status. While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: 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 Life at Unity Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com . Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know. This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English. This posting is intended to fill an existing vacancy, and we are committed to providing applicants with updates throughout the hiring process in accordance with applicable law. Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity. Your privacy is important to us. Please take a moment to review our Prospect and Applicant Privacy Policies. Should you have any concerns about your privacy, please contact us at DPO unity.com.

Requirements

Must-haves

What you ll be doing 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
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

Nice-to-haves

Nice to Have Experience with Kubernetes and cloud infrastructure (GCP or AWS)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningpythonkubernetesgcpaws

Platform: workday
Effort: long