> Markdown version of [/jobs/ext/1722169-staff-backend-engineer-ml-inference-systems](https://www.wearedevelopers.com/jobs/ext/1722169-staff-backend-engineer-ml-inference-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Backend Engineer, ML Inference Systems - **Company:** Unity Technologies - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Game Engine, Backend - **Published:** July 1, 2026 - **Apply:** https://dejobs.org/x/x/9034CE442C4F465C99C530B9649E53EA/job/ ## About the Role * 5+ years designing, deploying, and maintaining distributed systems at scale * Expertise in Golang for building high-performance, low-latency backend infrastructure * Hands-on experience with cloud infrastructure on GCP and workload orchestration with Kubernetes * Strong grounding in monitoring and observability tooling, including Prometheus and Grafana * Experience in ad tech, recommender systems, real-time personalization, or other performance-critical domains * Familiarity with microservice architectures, containerization (Docker), and CI/CD best practices * Familiarity with machine learning platforms, workflows, and serving infrastructure You might also have * Experience with ML inference servers like NVIDIA Triton Inference Server * Familiarity with auction mechanics or bidding systems in an ad tech context, 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. ## Description Our Vector Gamer AI team sits at the heart of that mission, governing ad ranking and bidding decisions across billions of daily impressions, where large-scale machine learning and real-world impact converge at scale., We're hiring a Staff Backend Engineer to build and operate the infrastructure those models depend on. You'll design and operate the distributed systems that power billions of daily decisions, with a focus on the performance, reliability, and scalability of inference systems. Join us and help influence how billions of gaming experiences are discovered, monetized, and how creators are rewarded. What you'll be doing * Design, develop, and deploy production-grade backend services and distributed systems powering large-scale online model inference at billions of daily requests * Drive technical direction of our inference platform, with a focus on low-latency, high-throughput serving infrastructure * Partner with ML engineers to ensure online serving infrastructure scales with growing model complexity and inference volumes, without compromising latency or throughput * Ensure the reliability, scalability, and efficiency of our systems in production using monitoring and observability tools like Prometheus and Grafana * Manage and optimize cloud infrastructure on GCP, orchestrating workloads with Kubernetes across a high-scale production environment * Promote and implement best practices for backend service development, testing, deployment, and monitoring (DevOps, SRE) ## Related Videos - [Building the platform for providing ML predictions based on real-time player activity](https://www.wearedevelopers.com/videos/944-building-the-platform-for-providing-ml-predictions-based-on-real-time-player-activity) - [Building a fully automated escape room](https://www.wearedevelopers.com/videos/735-building-a-fully-automated-escape-room) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Coding an Immersive Copilot using Unity / .NET and Azure OpenAI!](https://www.wearedevelopers.com/videos/1204-coding-an-immersive-copilot-using-unity-net-and-azure-openai) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Backend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/196-backend-developer-salary-in-germany-2023)