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Freelance Computer Vision Deep Learning Engineer Jobs in California

Matroid

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

About Matroid Matroid is a full-service computer vision company that has developed an end-to-end ... We are looking for a world-class Deep Learning Software Engineer who is excited to operate at the ...

Hayden AI

Deep Learning Engineer II

San Francisco, CA · On-site

$161K - $175K/yr

About Us At Hayden AI, we are on a mission to harness the power of computer vision to transform the ... Deep Learning Engineer II POSITION DUTIES: Lead the research, development, and deployment of ...

Matroid

Deep Learning Engineer

Palo Alto, CA

About Matroid Matroid is a full-service computer vision company that has developed an end-to-end ... We are looking for a world-class Deep Learning Software Engineer who is excited to operate at the ...

Imagry

Senior Deep Learning Engineer - Perception

San Jose, CA · On-site

$123K - $169K/yr

Senior Deep Learning Engineer, Computer Vision imagry.E4.E30@comeetapply.com Location: San Jose, CA , On Site We are looking for a capable and experienced Sr. Deep Learning Engineer to join our R&D ...

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Freelance Computer Vision Deep Learning Engineer information

What engineers make $500,000?

Senior computer vision deep learning engineers with extensive experience, advanced skills in neural networks, and proficiency in frameworks like TensorFlow or PyTorch can earn $500,000 or more annually, especially in high-demand industries such as autonomous vehicles, AI research, or tech giants. Achieving this level often requires a strong educational background, specialized certifications, and a track record of impactful projects.

Is computer vision a dead field?

Computer vision remains a vibrant and evolving field with ongoing research and practical applications, especially in areas like autonomous vehicles, medical imaging, and security. Freelance computer vision deep learning engineers are in demand for developing models using tools like TensorFlow and PyTorch, and staying current with advancements is essential for success.

Is ML full of coding?

Machine learning (ML) roles, including those for a freelance computer vision deep learning engineer, typically involve significant coding, especially in languages like Python and frameworks such as TensorFlow or PyTorch. Strong programming skills are essential for developing, training, and deploying models, although understanding algorithms and data preprocessing are also important components of the job.

Is ML a high paying job?

A career as a machine learning engineer, including roles in computer vision and deep learning, is generally considered high paying due to the specialized skills and demand for expertise in algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but these roles often offer competitive compensation compared to other tech positions.
What are the most commonly searched types of Computer Vision Deep Learning Engineer jobs in California? The most popular types of Computer Vision Deep Learning Engineer jobs in California are:
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Freelance Computer Vision Deep Learning Engineer jobs near you
Sr. Computer Vision Engineer (Deep Learning)

Sr. Computer Vision Engineer (Deep Learning)

Harbinger Motors Inc.

Mountain View, CA • On-site

$123K - $169K/yr

Other

Posted 28 days ago

Job description

We are seeking a highly skilled Senior Deep Learning Engineer to drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS). The successful candidate will play a key role in designing cutting-edge neural network architectures, optimizing model performance, and ensuring reliable deployment on embedded platforms. This position requires a balance of deep technical expertise, strong analytical thinking, and cross-functional collaboration. This role would sit in Mountain View, CA with PhantomAI, Harbinger's ADAS division. 


Responsibilities

  • Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems.
  • Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms.
  • Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions.
  • Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment.
  • Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs.
  • Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration.
  • Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management.


Required Qualifications

  • 5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications.
  • In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design.
  • Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience.
  • Strong proficiency in Python and a deep understanding of software design principles and development best practices.
  • Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
  • Practical experience with data pipelines, distributed training, and machine learning experiment management tools.
  • Proven ability to work effectively in a collaborative, cross-functional team environment.


Preferred Qualifications

  • Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods.
  • Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon).
  • Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation.
  • Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus.
  • Strong programming experience in Python and/or C++ within Linux development environments.
  • Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP)

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