Associate Director, MLOps Engineering
About this role
PathAI is building an AI-powered pathology platform designed to improve diagnostic accuracy and treatment efficacy for diseases such as cancer. The Machine Learning team seeks an Associate Director of MLOps Engineering to lead the group responsible for the foundational AI and machine learning stack. This visible leadership role involves overseeing infrastructure that bridges machine learning research and massive-scale production environments.
Key responsibilities include evolving the technical stack to support large-scale ML training and inference workloads, guiding team strategy, and partnering with cross-functional groups. The ideal candidate relishes technical challenges, excels at balancing short-term tactical needs with long-term architectural goals, and enjoys mentoring high-performing engineering talent.
What you will do
- Develop and execute the long-term vision and roadmap for the MLOps team to support company-wide development and deployment needs.
- Lead and mentor a team of six to seven or more high-performing engineers while strategically allocating resources.
- Partner with leaders across machine learning, data science, product engineering, and infrastructure to eliminate bottlenecks and deploy new solutions.
- Architect compute and storage pipelines that allow machine learning engineers to manage millions of slides without data fragmentation.
- Modernize the AI product inference stack to support significant growth in AI runs across global deployments.
- Collaborate with Site Reliability SRE teams to establish comprehensive metrics for compute utilization, network bottlenecks, and cost attribution.
- Conduct build-versus-buy assessments and stack refresh audits to benchmark internal tools against commercial and open-source alternatives.
What you need
- Holds a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or possesses equivalent experience.
- Brings eight to ten or more years of experience in software or machine learning engineering.
- Demonstrates at least four years of experience managing engineering teams and platform strategy.
- Shows a proven track record of growing teams, managing cloud budgets, and driving MLOps platform adoption.
- Possesses deep technical expertise with machine learning workloads on Kubernetes, major cloud computing platforms, workflow orchestration, and infrastructure-as-code principles.
- Exhibits demonstrated experience managing petabyte-scale datasets and high-throughput production inference pipelines.
- Displays strong software engineering skills in complex, multi-language systems along with scalable service architecture experience.
Nice to have
- Experience working with ML frameworks like PyTorch or Scikit-learn
- Experience with large-scale data processing frameworks such as Spark, Hive, Databricks, or 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 machine learning systems
Skills and tech stack
Questions candidates ask
What is the remote work and location policy for this role?
Boston (Onsite) is preferred, with New York (Onsite) or Remote options also available. This is a hybrid position based in Boston, MA, and relocation benefits are not provided.
What is the salary range for this position?
The expected salary range based on the primary location in Boston, MA, is $181,500 to $278,300. Actual pay is determined based on experience, qualifications, geographic location, and other job-related factors.
What kind of team will I be managing?
You will lead and mentor a team of 6-7+ high-performing engineers who are responsible for the backbone of the AI/ML stack. The position involves strategically allocating resources to manage support for existing services while executing key strategic initiatives.