Sr. Computer Vision Engineer (Deep Learning)

Harbinger Motors; Garden Grove (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Early-stage Stock Options

Robust Retirement Savings (401k, HSA, FSA)

Annual Vacation Bonus

Wellness & Fertility Benefits

Cell Phone Stipend

Complimentary Meals & Stocked Kitchens

Job summary

A pioneering electric vehicle company in California is seeking a Senior Computer Vision Engineer to lead the development of advanced deep learning models for Advanced Driver Assistance Systems (ADAS). The role requires over 5 years of experience in deep learning, knowledge of model optimization, and proficiency in Python and frameworks like PyTorch. This position offers competitive compensation ranging from $180,000 to $240,000, along with benefits such as stock options and a retirement savings plan.

Qualifications

• 5+ years of experience in deep learning for ADAS or computer vision.
• In-depth knowledge of training and inference processes.
• Strong software design and development practices.

Responsibilities

• Design advanced deep learning architectures for ADAS.
• Optimize existing models for performance and efficiency.
• Collaborate with engineering teams for seamless model integration.

Skills

Deep learning model development

Computer vision applications

Collaboration in cross-functional teams

Python programming

PyTorch or TensorFlow

Education

Advanced degree in Computer Vision, Robotics, or Machine Learning

Tools

PyTorch

TensorFlow

Embedded SoCs

Job description

Sr. Computer Vision Engineer (Deep Learning)

About Harbinger

Harbinger is an American commercial electric vehicle (EV) company on a mission to transform an industry starving for innovation. Harbinger’s best-in-class team of EV, battery, and drivetrain experts have pooled their deep experience to bring a first-of-its-kind EV platform to support the growing demand for medium‑duty EVs and Hybrids. Harbinger: Familiar Form, Revolutionary Foundation.

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).

Benefits

• Early‑stage Stock Options
• Robust Retirement Savings (401k, HSA, FSA)
• Annual Vacation Bonus
• Wellness & Fertility Benefits
• Cell Phone Stipend
• Complimentary Meals & Stocked Kitchens

$180,000 - $240,000 USD

Harbinger is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law.

Harbinger is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at [e-mail].