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Deep Learning Quantization Jobs (NOW HIRING)

$175K - $245K/yr

Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models. * Implement low-bit precision optimizations (e.g., INT8, BF16)

They are seeking a Machine Learning Engineer to translate research into scalable solutions ... 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 ...

Strong classical computer vision skills (geometry-based methods, feature extraction) complementing deep learning approaches. * Expertise in model acceleration, quantization, or compression (TensorRT ...

Design, develop/tune, and optimize deep learning models for ADAS computer vision features (e.g., pruning, quantization) and improve computational performance. * Plan and execute experiments to assess ...

Showing results 41-60

Deep Learning Quantization information

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$11K

$83.9K

$140K

How much do deep learning quantization jobs pay per year?

As of Aug 6, 2026, the average yearly pay for deep learning quantization in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

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.
More about Deep Learning Quantization jobs
What cities are hiring for Deep Learning Quantization jobs? Cities with the most Deep Learning Quantization job openings:
What states have the most Deep Learning Quantization jobs? States with the most job openings for Deep Learning Quantization jobs include:
Infographic showing various Deep Learning Quantization job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Staff Machine Learning Compiler Engineer

Rivian

Palo Alto, CA

$206K - $258K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 3 days ago


Rivian rating

7.3

Company rating: 7.3 out of 10

Based on 159 frontline employees who took The Breakroom Quiz

20th of 44 rated automakers


Job description

About Rivian

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. 

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. 


Role Summary

In this position you will be a key member of the ML Compiler team working on software tools to enable inference of deep learning networks hardware on Rivian Hardware Platforms. You will work closely with the Rivian Autonomy and Hardware teams and evaluate various implementation targeting for performance. You will help bring up new hardware and add support in the compiler for these hardware features.

This compiler enables HW-SW codesign and would result in developing efficient building blocks for state-of-the-art machine learning models. You will be collaborating with other cross functional teams in understanding the workloads, enabling running workloads on HW and help define the future enhancements to hardware and models.


Responsibilities
  • Lead the development of an ML Compiler for mapping Autonomy ML models to Rivian Autonomy Processor (RAP1).
  • Design and implement hardware-aware optimizations, including quantization strategies, model compression, memory-efficient representations, and operator fusion, targeted to RAP1.
  • Collaborate with hardware teams to co-optimize model architecture and compute pipeline under real-time constraints (latency, throughput, power).
  • Benchmark and analyze system performance across platforms and iterate to achieve optimal deployment efficiency.
  • Partner with autonomy teams to align model optimization efforts with hardware roadmap and real-world autonomy requirements.

Qualifications
  • Ph.D. or M.S. in Computer Engineering or a related field.
  • Excellent C/C++ and Python programming skills.
  • Experience with various SOC platforms used for machine learning.
  • Strong understanding of deep learning software models.
  • Experience in compiler pipeline development preferred.
  • Proficiency in deep learning frameworks and their low-level IRs or export formats.
  • Experience working in aggressive design environments is preferred.

Preferred Qualifications

  • Prior experience working with hardware-software co-design, especially for autonomous or robotics platforms.
  • Deep knowledge of numerical precision trade-offs, quantization-aware training (QAT), and dynamic/static quantization flows.
  • Familiarity with embedded real-time constraints and hardware profiling/debugging tools.
  • Familiarity with rearchitecting models to best suit hardware capabilities.

Pay Disclosure

Pay Range: The salary range for this role is $206,000 - $258,000 annually for Bay Area based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. The successful candidate may be eligible for annual performance bonus and equity awards.

Benefits: We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com.

You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than 6/20/2026.



Equal Opportunity

Rivian 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.

Rivian 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 candidateaccommodations@rivian.com.

Candidate Data Privacy and Technology

Rivian 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. Rivian 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. 

Rivian 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 People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. 

Rivian 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.  

How We Use AI in Our Hiring Process: To ensure transparency, we want candidates to know that Rivian uses iCIMS Talent Cloud Artificial Intelligence (TCAI) and AI-enabled tools to assist with screening, reviewing, organizing and highlighting profiles and applications that match the key requirements for each role.

AI does not make hiring decisions: Qualified candidate applications are reviewed by a member of our team, and all decisions throughout the process are made by humans. We use AI to support efficiency and consistency, not to replace human judgment. We are committed to a fair, thoughtful, and equitable experience for every candidate.
 

Participation in AI profile matching is entirely voluntary. If you prefer that your profile not be used in this process, you can opt out at any time. Opting out means your profile will be excluded from automated matching and will not be surfaced for additional roles through this system. Your current application remains active and will not be affected in any way.

Please note that we are currently not accepting applications from third party application services.

Qualifications:
  • Ph.D. or M.S. in Computer Engineering or a related field.
  • Excellent C/C++ and Python programming skills.
  • Experience with various SOC platforms used for machine learning.
  • Strong understanding of deep learning software models.
  • Experience in compiler pipeline development preferred.
  • Proficiency in deep learning frameworks and their low-level IRs or export formats.
  • Experience working in aggressive design environments is preferred.

Preferred Qualifications

  • Prior experience working with hardware-software co-design, especially for autonomous or robotics platforms.
  • Deep knowledge of numerical precision trade-offs, quantization-aware training (QAT), and dynamic/static quantization flows.
  • Familiarity with embedded real-time constraints and hardware profiling/debugging tools.
  • Familiarity with rearchitecting models to best suit hardware capabilities.
Education:UNAVAILABLEEmployment Type: FULL_TIME

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About Rivian

Sourced by ZipRecruiter

Rivian is a pioneering automotive industry player headquartered in Irvine, California. Established in 2009, the company has made notable advancements in developing sustainable transportation solutions. It is widely recognized for its electric adventure vehicles: the R1T pickup and the R1S SUV. Rivian is dedicated to creating a positive shift in societal mobility and emphasizes sustainability, innovation, and adventure as part of its core values. Their mission is to keep the world adventurous forever - a testament to their commitment in transitioning the world to sustainable transportation. Rivian's achievements are numerous, with one of the most notable being securing a significant multi-billion dollar investment from Amazon for the production of electric delivery vans.

Industry

Automobile dealers

Company size

10,000+ Employees

Headquarters location

Irvine, CA, US

Year founded

2009