Senior Machine Learning Engineer, ML Infrastructure- Online

Unity is a leading game engine company that powers games and 3D experiences across multiple industries. Unity is seeking a Senior Machine Learning Engineer to design and evolve its online model inference platform, build reliable production ML infrastructure, optimize inference performance, and enable safe experimentation. The role also involves improving observability, deployment reliability, scalability, and cost efficiency across online ML systems.

Responsibilities:

• 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 workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows
• Optimize model performance through model compilation, GPU/CPU utilization improvements, request scheduling, kernel fusion, and runtime-level tuning
• Improve observability of ML systems through latency, throughput, error-rate, cost, saturation, and model-health monitoring
• Partner closely with ML engineers to support faster model iteration while maintaining production safety, scalability, and cost efficiency
• Improve the reliability and reproducibility of model serving workflows, including model packaging, artifact validation, compatibility testing, and deployment automation
• Lead architectural improvements that make the online ML platform more robust, user-friendly, scalable, and cost-efficient

Requirements:

• 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
• Experience designing infrastructure for safe model rollout, canary testing, A/B experimentation, and automated rollback
• Strong systems thinking, with the ability to reason about latency, throughput, reliability, scalability, and cost tradeoffs in online systems
• Proven ability to lead technical direction and influence architectural decisions across teams without formal authority
• 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