Edge AI Research Engineer-Associate Staff

MIT Lincoln Laboratory•Lexington, MA

12d•$116,400 - $182,200

About The Position

The Embedded and AI Systems Group designs and prototypes cutting-edge AI and edge processing systems to meet the most demanding national security challenges. Our research team — comprising top experts from MIT Lincoln Laboratory and close collaborators from the DAF-MIT AI Accelerator — applies AI to sensor exploitation, human–AI teaming, decision support, and mission autonomy, with a strong emphasis on deploying AI at the tactical edge. In addition, the group develops tools and techniques for establishing trust in AI and applying AI to engineering design and scientific discovery. We take technologies from concept to prototype, and ultimately to real-world demonstrations on relevant platforms — bridging the gap between research and operational deployment. We develop advanced AI capabilities for ISR, edge sensing, autonomy, and decision-making systems, with a focus on transitioning early-stage research into working prototypes. Our work emphasizes deploying AI in real-world environments with constrained compute, power, and data. We are seeking an early-career AI Research Engineer to develop, optimize, and deploy machine learning models on resource-constrained (low size, weight, and power) platforms. This role focuses on bridging cutting-edge AI research with practical implementations that operate reliably under real-world constraints, including limited compute, noisy data, and degraded conditions. The ideal candidate is motivated to make AI systems work outside of idealized settings—balancing performance, efficiency, and robustness.

Requirements

Masters with 0–3 years of experience, or Bachelors with 3–5 years of experience, in Computer Science, Electrical Engineering, or a related field
Experience developing, training, adapting and assessing machine learning models
Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch)
Experience deploying ML models on edge or embedded systems
Understanding of tradeoffs between accuracy, compute, latency, and energy

Nice To Haves

Hands-on experience with model optimization techniques such as quantization, pruning, and distillation
Experience taking a model from training to deployed system is strongly preferred
Familiarity with edge AI toolchains (e.g., TensorRT, ONNX Runtime, CUDA)
Proficiency in C/C++ or other performance-oriented programming languages
Experience developing software in a Linux environment
Experience with CI/CD development pipelines
Experience working with real-world sensor data, including limited or noisy datasets
Experience evaluating system robustness under degraded conditions
Familiarity with test & evaluation methodologies for ML systems
Exposure to formal methods or verification approaches

Responsibilities

Investigate new algorithms, model architectures, and system approaches for efficient and robust AI
Develop research concepts into working prototypes
Develop and adapt machine learning models for deployment in low power and edge environments
Prototype and evaluate AI systems using limited, noisy, or imperfect real-world data
Collaborate with software and platform teams to integrate AI into end-to-end systems
Evaluate system performance, robustness, repeatability and resource usage under real-world conditions
Contribute to project leadership and participate in a culture of continuous learning, with opportunities to advance as a researcher and technical expert