Autonomy Engineer - Deep Learning Model Acceleration
Develops and optimizes deep learning inference infrastructure for real-time computer vision workloads on drones, focusing on high-throughput, low-latency performance across hardware platforms. Builds MLOps workflows, GPU kernels, and SDKs; requires strong DL, CV, and ML pipeline expertise.
About the job
How you’ll make an impact
- Develop solutions for high-performance deep learning inference for CV workloads that can deliver high throughput and low latency on different hardware platforms
- Profile CV and Vision Language Models (VLMs) to analyze performance, identify bottlenecks and acceleration/optimization opportunities and improve power efficiency of deep learning inference workloads
- Design and implement end to end MLOps workflows for model deployment, monitoring, and re-training
- Utilize advanced Machine Learning knowledge to leverage training or runtime frameworks or model efficiency tools to improve system performance
- Create new methods for improving training efficiency
- Implement GPU kernels for custom architectures and optimized inference
- Design and implement SDKs that allow customers/external developers to create autonomous workflows using Machine Learning (ML)
- Leverage your expertise and best-practices to uphold and improve Skydio’s engineering standards
What makes you a good fit
- Demonstrated hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment
- Strong knowledge of DL fundamentals, techniques, and state-of-the-art DL models/architectures
- Strong fundamentals in CV, image processing, and video processing
- Demonstrated hands-on experience building and managing ML pipelines for solving vision or vision language tasks including data preparation, model training, model deployment, and monitoring
- Experience and understanding of security and compliance requirements in ML infrastructure
- Experience with ML frameworks and libraries
- Demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
- Comfortable navigating and delivering within a complex codebase
- Strong communication skills and the ability to collaborate effectively at all levels of technical depth
Compensation
Annual base salary range: $170,000 - $277,500. Equity in the form of stock options, comprehensive benefits including health insurance, paid vacation, sick leave, holiday pay, and 401K.
Skills
Deep Learning, MLOps, Computer Vision, PyTorch, TensorFlow, Gpu Programming, Ml Inference Optimization, Edge Deployment, Vision Language Models, Ml Pipelines
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