Associate Director, MLOps Engineering

PathAI•Boston, MA

6d•$181,500 - $278,300•Hybrid

About The Position

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes. 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. The Opportunity: This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives.

Requirements

A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
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.
A proven track record of growing engineering teams, managing team budgets/cloud costs, and driving MLOps platform adoption across multi-disciplinary organization units.
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).
Demonstrated experience managing petabyte-scale datasets and high-throughput production inference pipelines.
Demonstrated 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.

Nice To Haves

Experience working 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.

Responsibilities

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.
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.
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.
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.
Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments.
Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution.
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.