Associate Director of MLOps Engineering, PathAI

• You have a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
• You have 8–10+ years in Software/ML Engineering, with 4+ years managing engineering teams and platform strategy; experience building production-grade frameworks for MLOps or ML Infrastructure
• You have a proven track record of growing engineering teams, managing team budgets/cloud costs, and driving MLOps platform adoption across multi-disciplinary organization units
• You have a demonstrated level of deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform)
• You have demonstrated experience managing petabyte-scale datasets and high-throughput production inference pipelines
• You have demonstrated strong software engineering skills in complex, multi-language systems and experience with scalable service architecture

Desirable

• We are seeking an Associate Director, MLOps Lead to join our Machine Learning team. In this position, you will lead the team who is responsible for the backbone of our AI/ML Stack. This is a highly visible role as you will oversee the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads
• The Associate Director MLOps Lead is someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better.. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between
• This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives
• Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth
• Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions
• Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency
• Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments
• System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution
• Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs