Engineering Team Lead

4 days ago


Cambridge, Massachusetts, United States Massachusetts Institute of Technology Full time
Job Title: Engineering Team Lead

Join the Massachusetts Institute of Technology (MIT) Quest for Intelligence team as an Engineering Team Lead, responsible for leading and supervising a team that builds and maintains artificial intelligence and machine learning (ML) computational platforms.

Job Summary

We are seeking a highly skilled and experienced Engineering Team Lead to lead our Systems Engineering Team. The successful candidate will be responsible for overall planning and execution of technical projects, managing/supervising a team from a range of disciplines, and developing complex processing pipelines that evaluate models framed as supervised learning, Bayesian inference, or reinforcement learning problems.

Responsibilities
  • Lead and supervise a team of engineers in building and maintaining AI/ML computational platforms
  • Develop and implement complex processing pipelines for model evaluation
  • Manage and supervise a team from a range of disciplines, including software development, data science, and engineering
  • Design and lead the integration of Quest systems and software platforms with cloud providers
  • Other duties as assigned
Requirements
  • 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
  • Strong project-management, analytical, problem-solving, organizational, decision-making, and written and verbal English communication skills
  • Ability to lead teams in a dynamic, unstructured environment
Preferred Qualifications
  • 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)
  • Experience with release management lifecycles, project milestones, managing execution, and high-quality product delivery


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