Machine Learning Software Engineer

About the role

About Us: Swan delivers the future of defense and industry through scalable autonomous products. Swan is backed by leading defense VCs including a16z's American Dynamism fund.

Role Overview: We are seeking an expert Machine Learning Engineer 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.

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.