Associate Director, MLOps Engineering

Role overview An Associate Director-level MLOps leadership role overseeing the infrastructure that bridges machine learning research and large-scale production within an AI-powered pathology platform. The position combines hands-on technical leadership with team management, focused on evolving the stack to support next-scale ML training and inference workloads for clinical and research deployments. Responsibilities Develop and execute the long-term vision and roadmap for the MLOps platform across business units, balancing tactical deliveries with architectural transformation. Lead, mentor, and allocate work for a team of 6–7+ engineers while managing budgets and cloud costs. Partner with machine learning, data science, product engineering, and infrastructure leaders to resolve bottlenecks and enable new solutions. Architect compute and storage pipelines for managing millions of pathology slides and complex derived artifacts. Modernize the inference stack to support 5–10x growth in AI runs across global deployments, in collaboration with Site Reliability Engineering. Requirements 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. Deep expertise with ML workloads on Kubernetes, cloud platforms (AWS, GCP, or Azure), workflow orchestration, and infrastructure-as-code (Helm, Terraform). Experience managing petabyte-scale datasets and high-throughput production inference pipelines. Strong software engineering skills in complex, multi-language systems and scalable service architecture. Experience using AI coding assistants across the platform development lifecycle. Nice to have Familiarity with ML frameworks such as PyTorch or Scikit-learn. Experience with large-scale data processing frameworks like 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 ML systems. Benefits and work setup Hybrid position based in Boston, MA; relocation benefits are not available. Expected salary range of $181,500–$278,300 based on experience, qualifications, geographic location, and other job-related factors. #J-18808-Ljbffr