Job description

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. 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. Who You Are: (Required) 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. You have experience using AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycles. Preferred: You have experience working with ML frameworks like PyTorch or Scikit-learn. You have experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR) You have demonstrated expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML. You have a familiarity with security and compliance best practices in ML systems. This is a hybrid position based in Boston, MA. Relocation benefits are not available for this position. The expected salary range for this position based on the primary location Boston, MA is $181,500 - $278,300. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

Requirements

Must-haves

PathAI's mission is to improve patient outcomes with AI-powered pathology.
This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives.
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.
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.
Who You Are: (Required)
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 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.
You have experience using AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycles.

Nice-to-haves

You have experience working with ML frameworks like PyTorch or Scikit-learn.
You have experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR)
You have demonstrated expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
You have a familiarity with security and compliance best practices in ML systems.
This is a hybrid position based in Boston, MA. Relocation benefits are not available for this position.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlkubernetesawsgcpazureci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision