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Salary: USD 181,500 - 278,300 / annual
PathAI is seeking an Associate Director, MLOps Lead to join its Machine Learning team. This is a highly visible leadership role overseeing the infrastructure that bridges ML research and production-scale deployment. You will lead a team of 6-7+ engineers responsible for the backbone of PathAI's AI/ML stack, with a primary focus on evolving the platform to meet next-generation scale requirements for large-scale ML training and inference workloads.
Key responsibilities include:
• Develop and execute the long-term vision and roadmap for the MLOps team, balancing short-term tactical deliveries with long-term architectural transformation for future growth.
• Lead and mentor a high-performing engineering team of 6-7+ engineers, strategically allocating resources to support existing services while executing key strategic initiatives.
• Partner cross-functionally with machine learning, data science, product engineering, and infrastructure leaders to identify pain points, address bottlenecks, and facilitate deployment of new solutions.
• Architect compute and storage pipelines to enable ML engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Modernize the AI product inference stack to support 5-10x growth of AI runs across global deployments.
• Collaborate with Site Reliability Engineering to establish comprehensive observability metrics covering compute utilization, network bottlenecks, and cost/turnaround-time attribution.
• Conduct "Build vs. Buy" assessments and lead "Stack Refresh" audits to benchmark proprietary tools against best-in-class commercial and open-source alternatives.
PathAI's mission is to improve patient outcomes with AI-powered pathology. The platform leverages modern machine learning and artificial intelligence to substantially improve the accuracy of diagnosis and efficacy of treatment for diseases like cancer. The company has a track record of success deploying AI algorithms for histopathology in translational research, pathology labs, and clinical trials.
REQUIREMENTS:
• Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience).
• 8–10+ years in Software/ML Engineering, with 4+ years managing engineering teams and platform strategy; production-grade experience building frameworks for MLOps or ML Infrastructure.
• Proven track record of growing engineering teams, managing team budgets/cloud costs, and driving MLOps platform adoption across multi-disciplinary organization units.
• 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).
• Demonstrated experience managing petabyte-scale datasets and high-throughput production inference pipelines.
• Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture.
• Experience using AI assistants (e.g., CoPilot, Cursor, Claude) across platform development lifecycles.
PREFERRED:
• Experience with ML frameworks like PyTorch or Scikit-learn.
• Experience with large-scale data processing frameworks (e.g., Spark, Hive, Databricks, Amazon EMR).
• Demonstrated expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
• Familiarity with security and compliance best practices in ML systems.
