AI/ML Engineer Multidisciplinary Engineering Design-Associate
Listed on 2026-05-05
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Engineering
Systems Engineer, AI Engineer (Applied/Software), Mechanical Engineer
AI/ML Engineer for Multidisciplinary Engineering Design-Associate Staff
Date: Apr 29, 2026
Location: Lexington, MA, US
Company: MIT Lincoln Laboratory
The Structural & Thermal-Fluids Engineering Group provides innovative multidisciplinary engineering solutions for underwater, ground, air, and space-based prototype systems for national security applications. Examples include the development of structural-material systems for space-based imaging and laser communication terminals, thermal management solutions spanning cryogenic to hypersonic applications, and aerodynamic designs and assessments to support unmanned aerial vehicles, airborne pods, and hypersonic platforms. Engineering solutions are developed through the utilization of appropriate technologies coupled with high-fidelity models and multidisciplinary simulations, environmental testing, and the utilization of advanced materials to meet ever increasing performance, size, weight, and environmental challenges.
Group 74 seeks engineers with a combination of artificial intelligence / machine learning (AI/ML) algorithm development and engineering modeling expertise, e.g., fluid, structural, or thermal modeling. Our engineers support a broad range of simulation, design, optimization, and test activities, and we develop innovative simulation capabilities to solve challenging engineering design problems across a broad array of applications ranging from low-speed aircraft to hypersonic systems and satellite design.
The Group employs machine learning methods within its modeling suite to achieve significant improvements in physics-based simulation, such as for aerodynamic analysis, to accelerate conceptual design. We seek engineers who are excited to contribute to these efforts by enhancing existing methods and implementing new cutting-edge AI/ML techniques to enable rapid and accurate concept design for a diverse set of complex and multidisciplinary problems.
This position offers opportunities to contribute to cutting-edge research, collaborate with leading experts, and develop innovative solutions to complex engineering challenges.
The successful candidate will be part of an interdisciplinary engineering team contributing to the development of operational prototype hardware, spanning the full program life cycle from concept development to fielding and testing. The position may require special security clearances and travel to meetings and field sites.
Minimum Qualifications- M.S. in Aerospace Engineering, Mechanical Engineering, Computer Science, or related. Candidates with B.S. degree with three years' experience will also be considered.
- Experience developing and applying novel artificial intelligence and machine learning algorithms to solve engineering or scientific applications, such as through physics-informed machine learning, data-driven surrogate modeling, reinforcement learning, or agentic AI.
- Proficiency in C++, Python, MATLAB, or similar for algorithm development and software integration.
- Experience applying engineering software to design and analysis, e.g., fluid, structural, or thermal simulations.
- Ability to work within an interdisciplinary team.
- Ability to clearly communicate results in oral presentations and written reports.
- Experience with high-performance computing (HPC) environments for large-scale simulations
- Experience implementing multidisciplinary design optimization (MDO) frameworks.
- Familiarity with software development best practices, including version control (e.g., Git).
Recent Graduate Hiring Range: $116,400 - $140,000
Experienced Hiring Range: $116,400 - $182,200
Disclaimer: MIT Lincoln Laboratory provides a typical hiring range as a good faith estimate of what we reasonably expect to offer for this position at the time of posting. The final salary offered to a selected candidate will depend on various factors, including but not limited to the scope and responsibilities of the role, the candidate's experience, skills and education/training, internal equity considerations and applicable legal requirements.
This range reflects base salary only and does not include additional forms of compensation or…
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