Autonomy Engineer - Deep Learning Infrastructure
Autonomy Engineer - Deep Learning Infrastructure
Posted today
SENIORITY
Lead
About the role
- 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
- You have demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
- You are comfortable navigating and delivering within a complex codebase
- Strong communication skills and the ability to collaborate effectively at all levels of technical depth
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
