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Evening Computer Vision Deep Learning Engineer Jobs

Deep Learning Engineer

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 ... About the Role As a Deep Learning Engineer at Hayden, you will make key contributions towards ...

Senior Deep Learning Engineer

$107K - $146K/yr

They are seeking a Senior Deep Learning Engineer to implement core algorithms at the intersection ... Responsibilities : • Implement core deep-learning, computer vision, and (inverse-)procedural ...

They are seeking a Deep Learning Engineer to implement core algorithms at the intersection of computer vision and computer graphics, focusing on manipulating large 2D and 3D media datasets.

About Us At Hayden AI, we are on a mission to harness the power of computer vision to transform the ... About the Role As a Staff Deep Learning Engineer in the Deep Learning team at Hayden, you are a ...

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Evening Computer Vision Deep Learning Engineer information

See salary details

$48.5K

$121.5K

$137.5K

How much do evening computer vision deep learning engineer jobs pay per year?

As of Jul 21, 2026, the average yearly pay for evening computer vision deep learning engineer in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.

What is the difference between Evening Computer Vision Deep Learning Engineer vs Computer Vision Deep Learning Engineer?

AspectEvening Computer Vision Deep Learning EngineerComputer Vision Deep Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with deep learning frameworksBachelor's or Master's in CS, AI, or related fields; experience with deep learning frameworks
Work EnvironmentTypically evening or night shifts, often in research labs or tech companiesStandard daytime hours, in offices or remote settings
Industry UsageUsed in industries with 24/7 operations like surveillance, security, or manufacturingCommon across tech, automotive, healthcare, and research sectors

The main difference lies in work hours and shift timing. Evening Computer Vision Deep Learning Engineers work primarily during evening or night shifts, often in environments requiring 24/7 monitoring or operations. In contrast, Computer Vision Deep Learning Engineers usually work standard daytime hours. Both roles require similar skills and educational backgrounds, but their schedules and work environments differ significantly.

More about Evening Computer Vision Deep Learning Engineer jobs
What cities are hiring for Evening Computer Vision Deep Learning Engineer jobs? Cities with the most Evening Computer Vision Deep Learning Engineer job openings:
What are the most commonly searched types of Computer Vision Deep Learning Engineer jobs? The most popular types of Computer Vision Deep Learning Engineer jobs are:
What states have the most Evening Computer Vision Deep Learning Engineer jobs? States with the most job openings for Evening Computer Vision Deep Learning Engineer jobs include:
Infographic showing various Evening Computer Vision Deep Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $121,515 per year, or $58.4 per hour.
Senior Machine Learning Engineer, Perception

Senior Machine Learning Engineer, Perception

PlusAI

Santa Clara, CA • On-site

$145K - $200K/yr

Full-time

Posted 27 days ago


Job description

We are seeking a highly skilled Machine Learning Engineer with deep expertise in developing Bird's Eye View (BEV) fusion models using multimodal sensor inputs, particularly LiDAR. You will play a central role in designing scalable perception algorithms that integrate data from camera, LiDAR, and radar sensors to support autonomous driving and 3D scene understanding.
Responsibilities:
  • Design, implement, and optimize BEV-based perception models that fuse camera, LiDAR, and radar inputs.
  • Benchmark perception models using large-scale datasets and well-defined quantitative metrics.
  • Collaborate cross-functionally with research, data, and deployment engineers to refine models and support real-world applications.
  • Maintain a strong focus on performance, robustness, and scalability for deployment in production systems.
  • Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
  • Ensure team compliance with QMS, monitor quality, and drive process improvements.
Required Skills:
  • Ph.D. or Masters in AI, Computer Science, Electrical Engineering, Robotics, or a related field.
  • Ph.D. new grad or Masters + 3 years industry experience
  • Proficiency in Python and experience building deep learning pipelines.
  • Strong expertise in PyTorch, TensorFlow, or JAX.
  • Proven experience with LiDAR-based 3D perception and BEV representation models
  • Deep understanding of multimodal sensor fusion architectures and techniques.
  • Familiarity with camera, LiDAR, and radar modalities and their synchronization, calibration, and integration in perception pipelines.
  • Solid foundation in computer vision, deep learning, and 3D geometry.
Preferred Skills:
  • Industry or academic experience in autonomous vehicle perception, robotics, or related areas.
  • Hands-on experience developing deep learning models in real-world or production environments.
  • Experience with distributed training, high-performance computing, or GPU acceleration.
$145,000 - $200,000 a year
Our compensations (cash and equity) are determined based on the position, your location, qualifications, and experience.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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