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Deep Learning Quantization Jobs in California (NOW HIRING)

Develop deep learning models for prototyping and production purposes according to product feature ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

Develop deep learning models for prototyping and production purposes according to product feature ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... quantization, deployment optimization). * Experienced in inference time optimization, deep ...

... deep learning systems, model deployment, and edge inference for real-world autonomous driving applications. Key Responsibilities * Support model quantization and deployment efforts for large-scale ...

... deep learning systems, model deployment, and edge inference for real-world autonomous driving applications. Key Responsibilities * Support model quantization and deployment efforts for large-scale ...

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $167K/yr

... deep learning approaches. • Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime). • Familiarity with real-time frameworks and middleware such as ROS 2, GStreamer ...

Role Summary We are looking for a Research Scientist with deep expertise in quantized deep learning ... Design and implement hardware-aware optimizations, including quantization strategies, model ...

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Deep Learning Quantization information

What are the key skills and qualifications needed to thrive as a Deep Learning Quantization Engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
What cities in California are hiring for Deep Learning Quantization jobs? Cities in California with the most Deep Learning Quantization job openings:
Infographic showing various Deep Learning Quantization job openings in California as of May 2026, with employment types broken down into 6% Internship, and 94% Full Time. Highlights an 94% In-person, and 6% Remote job distribution.
Sr. Computer Vision Engineer (Deep Learning)

Sr. Computer Vision Engineer (Deep Learning)

Harbinger Motors Inc.

Mountain View, CA • On-site

$180K - $240K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 22 days ago


Job description

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)

Key Benefits & Perks:
  • Comprehensive Health, Dental & Vision (HDV) - 100% employee covered
  • Early-stage Stock Options
  • Robust Retirement Savings (401k, HSA, FSA)
  • Generous Paid Time Off (PTO) & Parental Leave
  • Annual Vacation Bonus
  • Wellness & Fertility Benefits
  • Cell Phone Stipend
  • Complimentary Meals & Stocked Kitchens

California Pay Range
$180,000-$240,000 USD
Equal Opportunity
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 info@harbingermotors.com.
Candidate Data Privacy
Harbinger may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes ("Candidate Personal Data"). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Harbinger may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law.
Harbinger may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our HR, legal, and finance teams, and the team(s) with the position(s) for which you are applying; (ii) Harbinger affiliates; and (iii) Harbinger's service providers, including providers of background checks, staffing services, and cloud services.
Harbinger may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions.
Please note that we are currently not accepting applications from third party application services. Any unsolicited resumes or candidate profiles submitted in response to our job posting shall be considered the property of Harbinger and are not subject to payment of referral or placement fees if any such candidate is later hired by Harbinger unless you have a signed written agreement in place with us which covers the applicable job posting.