Senior Machine Learning Infrastructure Engineer

Unity Technologies•Bellevue, WA

13h•Remote

About The Position

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.

Requirements

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

Nice To Haves

Experience with feature stores, feature pipelines, or online/offline feature serving
Background in ad tech, real-time bidding, or programmatic advertising systems
Familiarity with infrastructure-as-code such as Terraform
Experience with observability tooling — Prometheus, Grafana, OpenTelemetry
Background with real-time data pipelines, caching layers, or low-latency serving systems

Responsibilities

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