Staff Deep Learning Engineer, Perception

Hayden AI Technologies, Inc.; San Francisco (CA); On-site; USD 130,000 - 180,000; Full time

Job summary

A leading company at the forefront of computer vision seeks a Staff Deep Learning Engineer to enhance a cutting-edge perception system. This pivotal role involves developing and refining deep learning and computer vision algorithms. Candidates should have a robust educational background, strong programming skills, and experience deploying models in real-world systems. Join a mission to transform transit systems with innovative AI-driven solutions.

Qualifications

• Proven experience in deploying deep learning systems.
• Track record in automated data annotation for computer vision.
• Skills in designing multi-modal deep learning models are a plus.

Responsibilities

• Drive the perception system development life cycle from definition to deployment.
• Develop advanced computer vision algorithms for urban scene perception.
• Collaborate with cross-functional teams to ensure model integration.

Skills

Computer Vision

Deep Learning

Data Science

Software Design

Problem-Solving

Education

Ph.D. or Master's in Robotics, Machine Learning, Computer Science, or Electrical Engineering

Tools

PyTorch

TensorFlow

OpenCV

TensorRT

Pandas

Job description

About Us

At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.

From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.

What the job involves

As an Staff Deep Learning Engineer, you will play a critical role in the development and refinement of a cutting-edge perception system, leveraging deep learning for real-world applications. Your expertise in computer vision, deep learning, and team leadership will drive performance improvements and seamless integration across the company.

Responsibilities

• Drive the entire perception system development life cycle, from problem definition to deployment and ongoing improvement.

• Actively contribute to the development and refinement of the perception system in a hands-on manner.

• Develop robust computer vision algorithms for object detection, tracking, semantic segmentation, and classification.

• Design and train deep learning models for complex urban scene perception and real-time analysis.

• Collaborate with cross-functional teams (cloud/device) for seamless integration and monitoring of perception models.

• Analyze data to identify performance bottlenecks and opportunities for enhancing the perception system.

• Automate improvement cycles of deep learning models used within the perception system.

• Communicate technical findings and insights effectively to stakeholders across the company to drive performance improvements.

• Utilize data visualization tools to present complex information clearly for informed decision-making.

Qualifications

• Ph.D. or Master's in Robotics, Machine Learning, Computer Science, Electrical Engineering, or a related field.

• Proven ability to deploy these systems with:

  • Deep Learning Frameworks: Expertise in PyTorch or TensorFlow (one mandatory, familiarity with both a plus).

  • Computer Vision Libraries: OpenCV.

  • Deployment Optimization Tools: TensorRT.

• Strong Python programming and software design with experience in Pandas.

• Experience deploying DL models to run on real-world, resource-constrained, systems with a pragmatic approach towards problem-solving.

• Demonstrated proficiency in data science and traditional machine learning (SVMs, Random Forests). Prior experience with automated machine learning pipelines is desirable.

• Proven industry track record with experience in:

  • Automated data annotation for computer vision.

  • Training multi-task and semi-supervised deep learning models for video data.

• Familiarity with designing multi-modal deep learning models incorporating temporal context and geometrical constraints is a plus.