Edge AI Research Engineer — Associate Staff (Low-Power Edge)

MIT Lincoln Laboratory; Lexington (MA); On-site; USD 116,000 - 182,000; Full time

Benefits offered by this job

Health plans

MIT‑funded pension

Matching 401K

Paid leave

Tuition reimbursement

Job summary

MIT Lincoln Laboratory's Embedded and AI Systems Group seeks an early‑career Edge AI Research Engineer to develop, optimize, and deploy ML models on resource‑constrained platforms. This role focuses on bridging cutting‑edge AI research with practical implementations that operate under real‑world constraints.

You will collaborate with software and platform teams to integrate AI into end‑to‑end systems, balance accuracy, compute, latency, and energy, and contribute to project leadership while

Qualifications

• MS or BS with relevant ML/AI/DL coursework and research.
• Experience training and evaluating ML models.
• Experience deploying ML on edge/embedded systems.
• Proficiency in Python and ML frameworks.

Responsibilities

• Investigate new algorithms and models for efficient, robust AI.
• Develop concepts into working prototypes.
• Adapt ML models for low power edge environments.
• Prototype and evaluate AI systems with noisy real-world data.
• Collaborate with software/platform teams to integrate AI.
• Evaluate performance, robustness, and resource usage.
• Contribute to project leadership and continuous learning.

Skills

ML model development

Python programming

PyTorch

Edge deployment

Tradeoff optimization

Education

Master's degree in Computer Science or Electrical Engineering

Bachelor's degree in Computer Science or Electrical Engineering

Tools

TensorRT

ONNX Runtime

CUDA

Job description

MIT Lincoln Laboratory's Embedded and AI Systems Group seeks an early‑career Edge AI Research Engineer to develop, optimize, and deploy ML models on resource‑constrained platforms. This role focuses on bridging cutting‑edge AI research with practical implementations that operate under real‑world constraints.

You will collaborate with software and platform teams to integrate AI into end‑to‑end systems, balance accuracy, compute, latency, and energy, and contribute to project leadership while