Machine Learning Software Engineer

Benefits

Parental leave

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Software Engineer Remote, United States 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.

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