Job Description

Senior MLOps Engineer

We are hiring for an MLOps Engineer for a fast-moving AI startup who are building a worldclass AI-powered video platform.

We are looking for a skilled and hands-on MLOps Engineer to join their growing team. You will play a critical role in deploying, scaling, and maintaining their machine learning infrastructure, supporting a range of tools that enable the controlled generation of high-quality animated videos.

Key Responsibilities

• Design, deploy, and maintain scalable training and data-processing pipelines on distributed compute clusters (e.g., Slurm, Kubernetes, or cloud-native equivalents).
• Optimize inference systems for latency and cost in a production setting.
• Collaborate closely with ML researchers and engineers to productionize deep learning models.
• Implement robust monitoring, logging, and alerting systems for model performance and infrastructure reliability.
• Automate model testing, validation, and deployment processes across staging and production environments.
• Ensure efficient usage of compute resources, including GPU clusters, and help identify bottlenecks or cost-saving opportunities.

Requirements

• Proven experience in MLOps, ML infrastructure, or related roles.
• Deep expertise in deploying and maintaining ML training pipelines on distributed systems.
• Strong knowledge of inference optimization techniques, especially in reducing latency and cost at scale.
• Proficiency with cloud platforms (AWS, GCP, Azure) and orchestration tools (Kubernetes, Docker).
• Experience working with GPU scheduling, distributed training (e.g., PyTorch DDP), and model serving frameworks (e.g., Triton, TorchServe).
• Familiarity with CI/CD for ML workflows.
• Strong Python skills and experience with ML/DL frameworks like PyTorch or TensorFlow.

Bonus Points

• Experience working in the creative media or animation industry.
• Exposure to video processing, generative AI, or large-scale content production systems.
• Experience collaborating with research teams or integrating research code into production pipelines.

Please apply for more information

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