Unity Technologies
Sr. Machine Learning Engineer, Infrastructure
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Job Description
The opportunity
Unity is looking for a Senior Machine Learning Infrastructure Engineer to join our Vector Ads team, where we build the real-time systems that power Unity's global advertising platform. This is a high-scale, low-latency environment — processing billions of requests daily to deliver fast, relevant ads to players around the world.
You'll build and operate the infrastructure that brings ML models from training into production, ensuring our ranking, bidding, and targeting systems run reliably at scale. This is a great opportunity for an engineer who's excited to work at the intersection of ML systems and distributed infrastructure, collaborate across teams, and have direct impact on how machine learning shapes the player and advertiser experience.
What you'll be doing
- Design, build, and maintain the infrastructure that serves ML models in real-time across Unity's ads ecosystem
- Build and operate scalable model serving pipelines — owning latency, throughput, and reliability in a high-QPS production environment
- Partner with ML engineers to productionize models, manage model deployments, and improve iteration speed
- Improve observability, performance, and cost-efficiency of ML serving infrastructure
- Contribute to architectural decisions around feature serving, model versioning, and inference optimization
What we're looking for
- Experience building and operating ML infrastructure or model serving systems in production
- Proficiency in Golang or Python, with strong systems engineering fundamentals
- Hands-on experience with Kubernetes and container orchestration at scale
- Familiarity with ML serving frameworks such as Ray Serve, Triton, TorchServe, or similar
- Understanding of distributed systems, API design, and system reliability
- Strong collaboration and communication skills in a remote-first environment
You might also have
* Experience with feature stores, feature pipelines, or online/offline feature serving *...
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