Senior Machine Learning Engineer, Unity

• Background in Computer Science, Machine Learning, Statistics or a related field
• Extensive hands-on experience designing, deploying, and maintaining ML systems at scale, including managing cloud infrastructure on GCP and orchestrating workloads with Kubernetes
• Strong track of record building ML platforms and infrastructure, including MLOps best practices — from data pipelines and model training workflows to serving and observability — with hands-on experience using tools like NVIDIA Triton Inference Server
• Expertise in Python and familiarity with production-grade ML frameworks (such as PyTorch or TensorFlow), as well as modern ML architectures (transformer-based models, embeddings, retrieval-augmented pipelines)
• Familiarity with robust MLOps practices, data pipelines, and cloud ML deployment, including model serving and monitoring
• Excellent communication and collaboration skills, with an ability to articulate technical concepts to both technical and non-technical audiences

Desirable

• We are seeking a Senior Machine Learning Engineer to make significant contributions to the next-generation of ML systems for Unity Ads
• In this role, you will focus on building the next-generation ML platform that powers our high-scale ad-tech models, from training to inference
• You will be deeply involved in the design, development, and deployment of ML models and the underlying platform, collaborating closely with product, engineering, and research teams to advance our ML capabilities
• Design, develop, and deploy scalable, production-grade ML systems and the platform components that support them, to optimize ad delivery, bidding strategies, and user engagement
• Contribute significantly to the technical roadmap and design of our ML platform, focusing on solutions for training and inference of high-scale ad-tech models
• Translate state-of-the-art research into practical, scalable solutions for our ML platform and models, ensuring Unity remains at the forefront of ML performance and innovation in the ad tech space
• Partner cross-functionally with data science, product, and engineering teams to align ML platform capabilities and model development with strategic business goals
• Collaborate on defining and refining requirements for data infrastructure, MLOps tooling, and processes to support advanced ML initiatives and the ML platform
• Promote and implement best practices for model development, testing, deployment, monitoring, and overall MLOps within the new platform
• Specifically focus on building and enhancing the ML platform capabilities for training, versioning, deploying, and serving high-scale ad-tech models, leveraging technologies such as Golang for performance-critical services and NVIDIA Triton Inference Server for optimized model serving