Distillation Lead
$195K - $286K/yr
... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...
Distillation Lead
$195K - $286K/yr
... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...
Distillation Lead
$195K - $286K/yr
... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...
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Distillation Lead
$195K - $286K/yr
... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...
Engineer/Senior Engineer - Perception Capabilities
$136K - $225K/yr
Solid understanding of fundamental deep learning concepts and computer vision techniques ... Experience with TensorRT and model quantization The salary range for this role is an estimate based ...
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Engineer/Senior Engineer - Perception Capabilities
$136K - $225K/yr
Solid understanding of fundamental deep learning concepts and computer vision techniques ... Experience with TensorRT and model quantization The salary range for this role is an estimate based ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
Quick apply
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
The role spans both deep technical work and collaboration with teams closest to the customer. As a ... TensorRT, quantization) * Work with Operations and Product to understand customer needs and ...
Senior / Staff ML Training Optimization Engineer
Pittsburgh, PA · On-site +1
$141K - $249K/yr
Examples include designing new CUDA kernels, quantization-aware training and inference, and ... deep learning frameworks such as PyTorch or Jax. - Skilled in profiling CPU and GPU code using ...
Quick apply
Senior / Staff ML Training Optimization Engineer
Pittsburgh, PA · On-site +1
$141K - $249K/yr
Examples include designing new CUDA kernels, quantization-aware training and inference, and ... deep learning frameworks such as PyTorch or Jax. - Skilled in profiling CPU and GPU code using ...
Senior / Staff ML Training Optimization Engineer
Pittsburgh, PA · On-site +1
$141K - $249K/yr
Examples include designing new CUDA kernels, quantization-aware training and inference, and ... deep learning frameworks such as PyTorch or Jax. - Skilled in profiling CPU and GPU code using ...
Senior / Staff ML Training Optimization Engineer
Pittsburgh, PA · On-site +1
$141K - $249K/yr
Examples include designing new CUDA kernels, quantization-aware training and inference, and ... deep learning frameworks such as PyTorch or Jax. - Skilled in profiling CPU and GPU code using ...
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?
| Aspect | Deep Learning Quantization | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Advanced degrees in AI, Computer Science, or related fields; knowledge of neural networks | Bachelor's or Master's in CS, Data Science, or related fields; programming skills |
| Work Environment | Research labs, AI development teams, hardware optimization settings | Software development teams, data-driven projects, product-focused environments |
| Industry Usage | AI hardware optimization, model deployment, edge computing | Model 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 are popular job titles related to Deep Learning Quantization jobs in Pennsylvania? For Deep Learning Quantization jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Pennsylvania look for? The top searched job categories for Deep Learning Quantization jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Deep Learning Quantization jobs? Cities in Pennsylvania with the most Deep Learning Quantization job openings:
$195K - $286K/yr
Full-time
Medical, Dental, Vision, PTO
Posted 25 days ago
Job description
Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.
With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai
Waabi's Physical AI platform is powered by state of the art ML models which must be deployed efficiently across diverse use-cases, from onboard vehicle inference to large-scale simulation. As the Distillation Lead, you will own the strategy and execution for distillation across Waabi's AI stack, ensuring our most capable models run efficiently in every deployment context. You will partner closely with ML Platform, Infrastructure, Onboard Autonomy, and Simulation teams to deliver compressed models that meet the performance requirements of both real-time onboard systems and high-throughput simulation pipelines.
You will...
- Define and drive the technical strategy for model distillation and compression across Waabi's AI stack - spanning perception, world models, and planning - with an eye toward both onboard deployment and simulation use-cases.
- Design, implement, and scale state-of-the-art distillation and efficiency pipelines, which may include:
Distillation for generative models (diffusion, autoregressive, flow-matching, video models)
Quantization-aware training (QAT) and post-training quantization (PTQ)
Knowledge distillation (feature-level, response-based, and relation-based)
Structured and unstructured pruning and sparsification
Low-rank factorization and efficient architecture design
Speculative decoding and other inference-time efficiency techniques
- Collaborate closely with ML Platform, Infrastructure, Onboard, Autonomy, and Simulation teams to integrate compressed models into production pipelines and meet latency, memory, and throughput targets across deployment contexts.
- Define rigorous benchmarks and evaluation frameworks to characterize efficiency vs. quality trade-offs across models and hardware targets.
- Mentor and guide researchers and engineers working in the distillation and model efficiency space, setting a high technical bar and fostering a culture of rigorous experimentation.
- Champion best practices for model compression across the organization; disseminate knowledge through internal design reviews, documentation, and technical talks.
- Stay at the cutting edge of model efficiency research; contribute to the broader scientific community through publications and open-source contributions.
Qualifications:
- Deep distillation expertise: You have extensive hands-on experience designing and implementing distillation, quantization, pruning, and model compression techniques for large-scale neural networks, with demonstrated impact in production settings.
- Strong research and engineering foundation: A Bachelor's or Master's degree in Machine Learning, Computer Vision, Robotics, or a related field, or equivalent industry experience; relevant hands-on experience in model distillation and efficiency is what matters most. Expert Python and PyTorch (or JAX) skills with experience in large-scale distributed training.
- Technical leadership: You have a proven track record of setting technical direction and driving projects from conception to production. You inspire and elevate those around you through deep technical expertise and mentorship.
- Cross-functional collaboration: You have experience working closely with infrastructure, platform, and autonomy teams to deploy compressed models under real engineering constraints.
- Clear communicator: You can communicate complex technical trade-offs clearly to diverse audiences and drive alignment across research and engineering teams.
Bonus:
- Experience with hardware-aware optimization (TensorRT, ONNX, custom CUDA kernels, hardware-specific quantization).
- Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas.
- Experience distilling large generative models (diffusion models, LLMs, VLMs, or video models).
- Background in autonomous vehicles or robotics.
The US yearly salary range for this role is: $195,000 - $286,000 USD in addition to competitive perks & benefits. Waabi (US) Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.
Perks/Benefits:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- As we grow, this list continues to evolve!
Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact!
Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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