Job description

Title: Senior/Staff Computer Vision Engineer - Deep Learning Focus Location: Mountain View, CA (4 days onsite a week) Duration: Full-time/Perm About Client: At Client, we've built a team of incredibly talented and ambitious people challenging the norm in the automotive industry. We are building cost-effective L2/L3 solutions to reduce the burden of everyday driving and make the roads safe for everyone. For instance, we believe democratizing technologies such as Automatic Emergency Braking and Emergency Lane Support is the first priority before tackling a fully self-driving vehicle. Our main customers are Tier 1 automotive manufacturers who are focused on delivering L2/L3 solutions and in the future will deliver full autonomy. We differentiate ourselves from other autonomous driving startups through a combination of state-of-the-art technological know-how and real automotive experiences of shipping ADAS systems at a volume production scale. If you feel that you have the passion, commitment, and drive to challenge the status quo within the automotive industry, we would love to hear from you. 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. 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 • 3–5+ 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) About Us: InterSources Inc , a Certified Diverse Supplier, was founded in 2007 and offers innovative solutions to help clients with Digital Transformations across various domains and industries. Our history spans over 16 years and today we are an Award-Winning Global Software Consultancy solving complex problems with technology. We recognize that our employees and our clients are our strengths as the diverse talents and opportunities they bring to the table enable us to grow as a global platform and they are causally linked with our success. We provide strategic and technical advice, and we have expertise in areas covering Artificial Intelligence, Cloud Migration, Custom Software Development, Data Analytics Infrastructure & Cloud Solutions, Cyber Security Services, etc. We make reasonable accommodations for clients and employees and we do not discriminate based on any protected attribute including race, religion, color, national origin, gender sexual orientation, gender identity, age, or marital status. We also are a Google Cloud partner company. We align strategy with execution and provide secure service solutions by developing and using the latest technologies that thrive our resources to deliver industry-leading capabilities to our clients and customers, making it convenient for our clients to do business with InterSources Inc. Our teams also drive growth by refining technology-driven client experiences that put the users first, providing an unparalleled experience. This results in strengthening the core technologies of clients, enabling them to scale with flexibility, create seamless digital experiences and build lifelong relationships.

Requirements

Must-haves

Title: Senior/Staff Computer Vision Engineer - Deep Learning Focus
Location: Mountain View, CA (4 days onsite a week)
Duration: Full-time/Perm
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.
3–5+ 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.
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.

Nice-to-haves

Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
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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Keywords to mirror

machine learningpython

Platform: jobdiva
Effort: medium

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Vision