Develop, deploy, and grow with Unity, the world’s leading 3D game engine. Build for all major platforms from mobile, to PC and console as well as XR, acquire players, monetize your game, and power industrial applications. Backed by Sequoia.
About the role
We are seeking a Senior ML engineer to design and evolve Unity Vector’s online model inference platform. This role focuses on building reliable infrastructure for serving machine learning models in production, optimizing inference performance, and enabling safe, efficient experimentation across high-traffic online systems. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Experience building and operating production-grade online ML inference systems, such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, TensorFlow Serving, or similar systems
- Experience with model serving frameworks such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, TensorFlow Serving, or similar systems
- Experience optimizing inference workloads using techniques such as dynamic batching, model compilation, quantization, GPU acceleration, GPU kernel optimization, caching, or runtime tuning
- Strong experience with distributed systems, Kubernetes, autoscaling, service reliability, and production observability
- Strong programming skills in Python, with practical experience working on production ML systems and high-scale services
- Experience with PyTorch and modern model deployment workflows, including model packaging, validation, and serving lifecycle management
More about this role
We are seeking a Senior ML engineer to design and evolve Unity Vector’s online model inference platform. This role focuses on building reliable infrastructure for serving machine learning models in production, optimizing inference performance, and enabling safe, efficient experimentation across high-traffic online systems.
You will work closely with ML engineers, platform teams, and product stakeholders to ensure models can be deployed, scaled, monitored, and iterated on efficiently. You will play a key role in shaping how models are packaged, served, validated, monitored, and optimized in production environments.
This role requires strong systems thinking, deep experience with production ML infrastructure, and the ability to drive architectural improvements across teams.
- Design and operate large-scale online inference infrastructure that serves production ML models with low latency and high reliability, such as PyTorch, Triton Inference Server, Kubernetes, GKE, Ray, or similar distributed serving frameworks.
- Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc.
- Integrate ML pipelines with...
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