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Engineering Team Lead
  • Job Number: 23805
  • Functional Area: Research - Engineering
  • Department: MIT Quest for Intelligence
  • School Area: Schwarzman College of Computing
  • Employment Type: Full-time (Hybrid)
  • Employment Category: Exempt
  • Visa Sponsorship Available: No
  • Schedule:


Job Description

ENGINEERING TEAM LEAD, MIT Quest for Intelligence-Systems Engineering Team, to lead and supervise a team that is building and maintaining artificial intelligence and machine learning (ML) computational platforms designed to advance Quest missions research, compose and benchmark models of natural intelligence, and assess the utility of systems in solving real problems of reasoning and decision-making.  Responsibilities include overall planning and execution of technical projects; managing/supervising a team from a range of disciplines; developing complex processing pipelines that evaluate models framed as supervised learning, Bayesian inference, or reinforcement learning problems; designing and leading the integration of Quest systems and software platforms with cloud providers; and other duties as assigned.  

Job Requirements

REQUIRED:  M.S. in computer science/ML/related engineering field; seven years’ related experience; experience with ML toolkits (e.g., PyTorch, TensorFlow, scikit-learn, MXNet); experience with at least one of the following--computer vision, optimization, time-series forecasting, natural language understanding, reinforcement learning, and/or visualization; experience with software development practices (e.g., git-based version control, CI/CD, etc.); strong project-management, analytical, problem-solving, organizational, decision-making, and written and verbal English communication skills; and ability to lead teams in a dynamic, unstructured environment.  PREFERRED:  Ph.D.; five years’ technical leadership and people management experience; two years’ program/project management experience; experience with brain and cognitive research and/or biological data (e.g., experiment design and execution, brain recordings, cognitive measurements); contributions to research communities/efforts, including published papers in ML (e.g., JMLR, NeurIPS, CVPR, ICML, ICCV, ICLR); experience with virtualization and containerization (e.g., Docker), implementing data storage and processing systems (e.g., Hadoop, SQL), and developing and deploying ML applications using a cloud service (e.g., AWS, GCP, Azure); and experience with release management lifecycles, project milestones, managing execution, and high-quality product delivery.  Job #23805

3/11/24