Machine Learning Developer

MIT Lincoln Laboratory•Lexington, MA

3d•$140,000 - $207,000•Hybrid

About The Position

The Transportation Safety and Resilience Group develops integrated sensing and decision support systems to support a safe, resilient global transportation system. Its work includes systems analysis, prototyping, system architectures, and algorithm development for supply chain resilience, aviation safety, and the integration of Advanced Air Mobility vehicles and other autonomous or semi-autonomous vehicles into the National Airspace System. The Group also develops systems to prevent aircraft accidents and runway incursions, detect-and-avoid systems for crewed and uncrewed aircraft, and decision support systems, including efforts to improve Department of War supply chain resilience. The position is on MIT Lincoln Laboratory’s Technical Staff. Technical Staff members perform innovative research supporting the Laboratory’s national security mission, prototype technology solutions, and contribute across the program lifecycle. Depending on level, they may lead projects, major technical workstreams, complex programs, or technical initiatives; translate Group strategy into research and execution plans; collaborate across disciplines; communicate with sponsors and leadership; and mentor other technical personnel. The successful candidate will develop applied engineering, modeling and simulation, and machine learning algorithms and systems for autonomous and semi-autonomous vehicles. The work includes developing advanced decision support systems, architectures, and techniques for deploying semi-autonomous systems in transportation. Example areas include collision avoidance, real-time contingency management, inter-vehicle coordination, and validation of decision support, surveillance, and tracking systems. The candidate will analyze operational problems, develop deployable solutions, lead and develop applied research and development programs, formulate new approaches to existing challenges, and publish research results. The Group supports a hybrid work environment.

Requirements

PhD in engineering, physics, mathematics, computer science, or a similar field; alternatively, a Master’s degree with four years of relevant experience will be considered.
Experience with state-of-the-art machine learning approaches and architectures.
Experience developing algorithms and architectures for the advancement and deployment of autonomous systems.
Experience with reinforcement learning.
Publication record with conference and/or journal articles.
Experience with algorithmic software development in Python.
Ability to collaborate as part of a team and good interpersonal skills.
Ability to effectively distill and communicate concepts and results to a wide audience.
For Technical Staff Level 5: Bachelor’s degree in a STEM or relevant technical field and typically 6 or more years of relevant experience; or Master’s degree and typically 4 or more years; or PhD and typically 0 or more years; or equivalent education and experience.
For Technical Staff Level 6: Bachelor’s degree in a STEM or relevant technical field and typically 8 or more years of relevant experience; or Master’s degree and typically 6 or more years; or PhD and typically 2 or more years; or equivalent education and experience.
Selected candidate must be able to obtain and maintain a Secret-level DoD security clearance and is subject to a pre-employment background investigation.
U.S. citizenship is required.

Nice To Haves

Knowledge of the air or surface transportation domain.
Experience developing algorithms and/or mathematical models in a simulation environment and/or deploying and testing on hardware.
Experience with Natural Language Processing, Natural Language Understanding, or Automatic Speech Recognition.
Experience with programming languages such as C++, MATLAB, or Julia, in addition to Python.
Publications in air transportation-related conferences or journals.
Experience leading a small team.
Experience with distributed computing and scalable machine learning frameworks.
Experience submitting research proposals.

Responsibilities

Develop algorithms and systems for autonomous and semi-autonomous vehicles using applied engineering, modeling and simulation, and machine learning.
Develop advanced decision support systems, architectures, and techniques for advancing and deploying semi-autonomous systems in transportation.
Develop solutions in areas such as collision avoidance, real-time contingency management, inter-vehicle coordination, and validation of decision support, surveillance, and tracking systems.
Analyze operational problems and develop deployable solutions for the transportation domain.
Lead and develop applied research and development programs in transportation.
Formulate new approaches to existing challenges and publish results in conferences and journals.
Depending on level, lead projects, major workstreams, complex programs, or multi-team technical efforts.
Collaborate across multidisciplinary teams, communicate technical findings and recommendations, and contribute to program documentation, proposals, reports, and presentations.
Mentor Associate Technical Staff, Assistant Technical Staff, and other technical support personnel.