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Deep Learning Quantization Jobs in Massachusetts

Pwc

AI Engineer

Boston, MA · On-site

$50K - $112K/yr

... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Zoox

Software Engineer - C++ GPU Performance

Boston, MA · On-site

$168K - $239K/yr

The scope for GPU usage ranges from traditional computer vision and deep learning architectures to ... Hands-on work with ML model optimization (post-training quantization, layer pruning, etc) or hand ...

Zoox

Software Engineer - C++ GPU Performance

Boston, MA · On-site

$168K - $239K/yr

The scope for GPU usage ranges from traditional computer vision and deep learning architectures to ... Hands-on work with ML model optimization (post-training quantization, layer pruning, etc) or hand ...

Zoox

Software Engineer - C++ GPU Performance

Boston, MA · On-site

$168K - $239K/yr

The scope for GPU usage ranges from traditional computer vision and deep learning architectures to ... Hands-on work with ML model optimization (post-training quantization, layer pruning, etc) or hand ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA

$142K - $187K/yr

Drive quantization and deployment readiness from an embedded perspective: * validate INT8/FP16 ... Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · On-site

$142K - $187K/yr

Drive quantization and deployment readiness from an embedded perspective: * validate INT8/FP16 ... Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA

$142K - $187K/yr

Drive quantization and deployment readiness from an embedded perspective: * validate INT8/FP16 ... Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture ...

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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 are popular job titles related to Deep Learning Quantization jobs in Massachusetts? For Deep Learning Quantization jobs in Massachusetts, the most frequently searched job titles are:
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Deep Learning Quantization jobs near you

Senior/Staff Software Engineer - Machine Learning & System Optimization

Zoox

Boston, MA

$226K - $307K/yr

Full-time

Re-posted 7 days ago

Job description

The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence.

As a Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another.

You will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs.

In addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.

In this role, you will:

  • Allocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot.

  • Spearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies.

  • Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA).

  • Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment.

  • Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems.

Qualifications:

  • Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput.

  • Deep expertise in working with real-time systems & required constraints such as processing latency, memory utilization, and memory bandwidth pressure.

  • Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).

  • Proficiency in low-level programming for AI accelerators, specifically developing and optimizing custom ML OPs and TensorRT Plugins with efficient CUDA kernel implementations.

  • Production-level C++ (14/17/20) and Python programming skills, with experience developing concurrent, memory-safe, real-time inference code for edge devices.

Bonus Qualifications:

  • Prior experience in high-performance robotics applications such as AV/drones/robots.

  • Familiarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).

  • Experience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).

Base Salary Range 
 
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

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Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.

A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

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.