About Us:
I'm working with a company building the future of defense and industry through scalable autonomous products. They're backed by leading defense-focused venture capital.
Role Overview:
We are seeking expert Machine Learning Engineers with deep experience in computer vision, model optimization, and deployment on low-cost embedded systems. The ideal candidate will have a strong background in designing, training, and optimizing deep learning models for real-time applications. This role requires expertise in efficient neural network architectures, quantization, model compression, and hardware acceleration techniques to run ML models on resource-constrained devices.
We're hiring at multiple levels - mid all the way through to staff/lead.
Key Responsibilities:
- Design, develop, and optimize computer vision models for real-time applications on embedded systems.
- Implement model compression techniques such as quantization, pruning, and knowledge distillation to improve performance on low-power hardware.
- Deploy machine learning models on embedded platforms, including ARM, NVIDIA Jetson, Qualcomm, or custom ASICs.
- Write clean, efficient, and well-documented code in Python and C++, leveraging ML frameworks like TensorFlow, PyTorch, and ONNX.
- Develop and fine-tune SLAM, object detection, tracking, and feature extraction models for high efficiency.
- Collaborate with cross-functional teams to integrate ML models into production systems, optimizing for latency, accuracy, and power consumption.
- Benchmark and profile ML models to identify and implement optimizations for inference on embedded hardware.
- Research and apply cutting-edge ML techniques to improve real-time performance in resource-constrained environments.
Qualifications and Skills:
- Master's or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field.
- 5+ years of experience in machine learning, deep learning, and computer vision.
- Extensive experience in designing and deploying optimized deep learning models for real-world applications.
- Proficiency in TensorFlow, PyTorch, ONNX, TensorRT, and other ML frameworks.
- Strong experience with model quantization, pruning, knowledge distillation, and hardware acceleration techniques.
- Solid programming skills in Python and C++, with a strong understanding of software optimization.
- Familiarity with embedded platforms such as NVIDIA Jetson, Raspberry Pi, ARM Cortex, Qualcomm AI accelerators, or specialized AI chips.
- Experience with hardware-aware model optimization to maximize inference speed and minimize memory footprint.
- Strong problem-solving skills and ability to work independently on complex technical challenges.
Preferred Qualifications:
- Experience with real-time SLAM, visual odometry, and multi-sensor fusion.
- Knowledge of low-level hardware acceleration using CUDA, OpenCL, or specialized ML accelerators.
- Familiarity with robotics frameworks such as ROS for integrating ML models into robotic systems.
- Background in edge AI deployments and optimizing neural networks for mobile and IoT devices.
What We Offer:
It's a fast-paced, innovative, and collaborative startup environment, with a top-notch benefits package including:
- Top-tier health, dental, vision, short-/long-term disability, and life insurance, with full employee coverage and partial coverage for dependents
- Flexible/reasonable vacation and sick leave
- 401(k) plans, FSA, HSA, and commuter benefits