Machine Learning Engineer for Autonomous Systems & RL

MIT Lincoln Laboratory; Lexington (MA); Hybrid; USD 145,000 - 220,000; Full time

Benefits offered by this job

Health plans

Pension

401K match

Paid leave

Tuition reimbursement

Job summary

MIT Lincoln Laboratory’s Transportation Safety and Resilience Group seeks applicants with a strong background in applied engineering, modeling, machine learning, and autonomous/ semi-autonomous vehicle systems. You will develop algorithms, architectures, and decision-support solutions for safe and efficient transportation, with opportunities to publish results in conferences and journals.

The role supports a hybrid work environment and emphasizes cross-disciplinary collaboration and real-world

Qualifications

• PhD in engineering, physics, mathematics, or computer science or similar field; Master’s with four years relevant experience considered in lieu.
• Experience with state of the art machine learning approaches and architectures.
• Experience developing algorithms and architectures for autonomous systems deployment.
• Experience with reinforcement learning.
• Publication record with conference/journal articles.
• Experience with algorithmic software development in Python.
• Ability to collaborate well as part of a team with good interpersonal skills.

Responsibilities

• Develop algorithms and systems for autonomous and semi-autonomous vehicles.
• Lead and develop applied research programs in transportation domain.
• Formulate new approaches to existing challenges and publish results.
• Design architectures and techniques for deploying semi-autonomous systems in transportation.

Skills

Collaboration skills

Great communication

Presentation skills

Education

PhD in engineering/physics/math/CS

Master’s +4 years experience

Tools

Python

C++

MATLAB

Julia

Job description

MIT Lincoln Laboratory’s Transportation Safety and Resilience Group seeks applicants with a strong background in applied engineering, modeling, machine learning, and autonomous/ semi-autonomous vehicle systems. You will develop algorithms, architectures, and decision-support solutions for safe and efficient transportation, with opportunities to publish results in conferences and journals.

The role supports a hybrid work environment and emphasizes cross-disciplinary collaboration and real-world