1

Deep Learning Quantization Jobs in Boston, MA (NOW HIRING)

Deep Learning Researcher 1900S-2

Billerica, MA ยท Hybrid

$130K - $160K/yr

Develop and evaluate novel deep learning models for complex physical and chemical systems in ... Knowledge of model efficiency techniques (pruning, quantization, distillation). * Familiarity with ...

Deep Learning Researcher 1900S-2

Billerica, MA ยท On-site

$130K - $160K/yr

Develop and evaluate novel deep learning models for complex physical and chemical systems in ... Knowledge of model efficiency techniques (pruning, quantization, distillation). * Familiarity with ...

Experience with embedded ML, quantization, or hardwareaware optimization. * Handson experience building and deploying efficient deep learning models for realworld computer vision applications.

Senior Machine Learning Engineer

Boston, MA ยท On-site +1

$174.19K - $287.41K/yr

... productize deep learning research. If you are someone who wants to contribute to solving ... Benchmark, profile, and evaluate different parallelizations, quantization and sparsification ...

Senior Machine Learning Engineer

Boston, MA ยท On-site +1

$174.19K - $287.41K/yr

... productize deep learning research. If you are someone who wants to contribute to solving ... Benchmark, profile, and evaluate different parallelizations, quantization and sparsification ...

Principal Machine Learning Engineer

Boston, MA ยท On-site +1

$189.60K - $312.73K/yr

Stay current with LLM architectures, inference optimizations, quantization research, and CPU/GPU hardware advancements. What you will bring * Strong understanding of machine learning and deep ...

Senior Machine Learning Engineer

Natick, MA ยท On-site

$115K - $225K/yr

Experience with embedded ML, quantization, or hardware-aware optimization. * Hands-on experience building and deploying efficient deep learning models for real-world computer vision applications.

Principal Machine Learning Engineer

Boston, MA ยท On-site +1

$189.60K - $312.73K/yr

Stay current with LLM architectures, inference optimizations, quantization research, and CPU/GPU hardware advancements. What you will bring * Strong understanding of machine learning and deep ...

Machine Learning Engineer, Distributed vLLM

Boston, MA ยท On-site +1

$136.32K - $287.41K/yr

... quantization and sparsification, our team provides a stable platform for enterprises to build ... If you want to solve cutting edge problems at the intersection of deep learning, distributed ...

next page

Showing results 1-20

Deep Learning Quantization information

See Boston, MA salary details

$11.9K

$91.1K

$152.1K

How much do deep learning quantization jobs pay per year?

As of May 28, 2026, the average yearly pay for deep learning quantization in Boston, MA is $91,133.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,200.00 and $151,000.00 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 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 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 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 are popular job titles related to Deep Learning Quantization jobs in Boston, MA? For Deep Learning Quantization jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Boston, MA look for? The top searched job categories for Deep Learning Quantization jobs in Boston, MA are:
Deep Learning Researcher 1900S-2

Deep Learning Researcher 1900S-2

E Ink

Billerica, MA โ€ข Hybrid

$130K - $160K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 8 days ago


Job description

Team up with the most innovative company where Imagination becomes reality!ย  E Ink is the originator, pioneer, and commercial leader in ePaper technology. The Billerica Research & Development team is thriving and growing as we help develop products that are changing how people access information every day.ย  We are seeking qualified candidates who are self-driven, looking to advance their career and become a high-impact player on a team.

Based on technology from MITโ€™s Media Lab, E Ink has transformed and defined the eReader market. Its Electrophoretic Display products make it the worldwide leader for ePaper. The Companyโ€™s corporate philosophy aims to deliver revolutionary products, user experiences and environmental benefits through advanced technology development.

Our diversity of people, backgrounds, experiences, thoughts and perspectives is fostered to create an inclusive work environment.

Our culture is built on value commitments to innovation, quality, results, integrity, community, people, and collaboration that fosters a strong employee engagement, teamwork, safety and wellness.

We offer aย competitive/generous benefits packageย that fits the needs of our employees. It includesย health,ย dental,ย vision,ย wellness programs, employee discounts,ย 401k matches, ongoing development, advancement opportunities and more. This position is alsoย eligible for our bonusย program.ย (see E Ink Our Company)

The Deep Learning Center at E Ink is seeking a Deep Learning Researcher to develop and improve product development and research of electrophoretic display technology. Your role will be to use and develop cutting edge deep learning models to guide materials discovery and display design. This work will take place in a rapidly growing team developing new processes to advance materials discovery and improve mass produced product performance. As a key team member, you will be expected to take on challenging exploratory projects that lead to product improvement and new products in a culture of open review and critical discussion. E Ink maintains a lean team where each member has strong visibility and responsibility to meet company goals.

Responsibilities:

  • Develop and evaluate novel deep learning models for complex physical and chemical systems in advanced materials and display technologies.
  • Formulate and test theoretically grounded hypotheses about model behavior and representation.
  • Build pipelines for experimental data processing, training, and evaluation.
  • Lead inverse design and model-based discovery efforts using Bayesian optimization, diffusion models, or related methods.
  • Collaborate with scientists to integrate domain knowledge into deep learning architectures and interpret results.
  • Create tools that make models and predictions transparent and usable.
  • Promote data quality, reproducibility, and experimental lineage across projects.

              Qualifications:

              • Ph.D. (or exceptional M.S. + 4 yearsโ€™ experience) in Physics, Applied Math, Computer Science, Electrical Engineering, or related field.
              • Demonstrated research experience in deep learning with a track record of innovation.
              • Proficiency in Python and major ML frameworks (e.g., PyTorch, TensorFlow, JAX).
              • Strong mathematical and problem-solving ability.
              • Effective collaborator with excellent communication skills and scientific curiosity.

                  Preferences:

                  • Experience designing custom architectures, meta-learning systems, or model-based optimization.
                  • Knowledge of model efficiency techniques (pruning, quantization, distillation).
                  • Familiarity with inverse design, Bayesian optimization, or generative models.
                  • Knowledge of colloid science, soft-matter physics, or chemical modeling.

                  Benefits:

                  • Competitive total compensation package
                  • Medical, dental and vision on 1st day
                  • Company 401K match
                  • 20 PTO days
                  • Generous sick leave policy
                  • Casual day to day work environment
                  • Hybrid/flexible work environment (for some positions)

                  E ink is committed to a diverse and inclusive workforce.ย  E Ink is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, identity, sexual orientation, veteranโ€™s status, disability, age, or on any basis prohibited by federal and state law.ย  ย 

                  Salary Range Disclosure: The annual base salary range for this position is $130,000 to $160,000 not including any variable pay. The total compensation package may include performance-based incentives, discretionary bonuses, and other variable pay components. The salary range for this position reflects a reasonable estimate at the time of posting and may vary based on factors such as experience, skills, education, certifications, and location.


                  E Ink logo

                  About E Ink

                  Sourced by ZipRecruiter

                  Industry

                  Electrical equipment, appliance, and component manufacturing

                  Company size

                  51 - 200 Employees

                  Headquarters location

                  Billerica, MA, US

                  Year founded

                  1997

                  Social media