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Deep Learning Accelerator Jobs in Arizona (NOW HIRING)

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

Join force with MLEs or firmware or hardware engineers to leverage hardware accelerators and ... Strong proficiency in programming languages such as Python, C/C++, experience with deep learning ...

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

Join force with MLEs or firmware or hardware engineers to leverage hardware accelerators and ... Strong proficiency in programming languages such as Python, C/C++, experience with deep learning ...

Physical AI Solutions Architect

Phoenix, AZ · On-site

$62.50 - $82.50/hr

... Intel CPUs, GPUs, AI accelerators, software, and platform technologies within customer ... architecture, deep learning models, and algorithm optimization for CPUs and GPUs. - Strong ...

Fosterculture of learning, skill building, agility, adaptability, experimentation, results ... Deep expertise in designing and activating product operating models, including hands-on experience ...

Digital Tools Application Engineer

Phoenix, AZ · On-site +1

$108K - $148K/yr

We empower our team to push the boundaries of what is possible-while learning every day in a ... Leverage a deep understanding of customer technologies and identify customer HVPs, working with ...

Deep Learning Accelerator information

What is a deep learning accelerator?

Deep Learning Accelerators are specialized hardware or systems designed to speed up the processing and training of deep learning algorithms, such as neural networks. They are optimized for the heavy computational demands of tasks like image recognition, natural language processing, and other AI applications. Examples include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed chips like Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs). These accelerators enable faster data processing, lower power consumption, and improved efficiency compared to general-purpose CPUs. As AI applications grow, the use of deep learning accelerators is becoming increasingly important in both research and industry.

What skills and qualifications are needed to thrive as a deep learning accelerator engineer?

To thrive as a Deep Learning Accelerator Engineer, you need a strong background in computer engineering, digital design, and machine learning, typically supported by a degree in computer science or electrical engineering. Experience with hardware description languages (such as Verilog or VHDL), FPGA/ASIC toolchains, and familiarity with deep learning frameworks like TensorFlow or PyTorch is essential. Problem-solving, teamwork, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating algorithmic requirements into efficient hardware solutions. These skills are vital to designing high-performance, energy-efficient hardware accelerators that advance AI capabilities and meet industry demands.

What are the main challenges faced when optimizing deep learning models for hardware accelerators?

One of the primary challenges in this role is bridging the gap between deep learning model requirements and the constraints of specialized hardware, such as GPUs, TPUs, or custom ASICs. This often involves model quantization, memory optimization, and adapting algorithms to exploit hardware parallelism while maintaining accuracy and efficiency. Collaboration with both hardware engineers and software developers is essential to ensure models run efficiently on target platforms, and staying current with evolving accelerator architectures is key to long-term success.

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

AspectDeep Learning AcceleratorMachine Learning Engineer
Required CredentialsKnowledge of hardware design, FPGA/ASIC programming, deep learning frameworksDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentHardware development labs, embedded systems, AI hardware companiesSoftware development environments, tech companies, research labs
Industry UsageAI hardware manufacturing, embedded AI solutionsAI/ML software development, data analysis, model deployment
Search & Comparison IntentFocus on hardware acceleration, AI hardware designFocus on software development, model building

Deep Learning Accelerators specialize in hardware design and optimization for AI workloads, working closely with hardware and embedded systems. Machine Learning Engineers develop and deploy ML models primarily through software, focusing on algorithms and data. While both roles involve AI, their core skills, work environments, and industry applications differ significantly.

What cities in Arizona are hiring for Deep Learning Accelerator jobs?

Cities in Arizona with the most Deep Learning Accelerator job openings:

Senior Machine Learning Scientist

Scottsdale, AZ • On-site

Axon
Public Safety Statistics Centers and Offices • 501 - 1,000 employees

$92K - $125K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 2 days ago


Axon rating

8.8

Company rating: 8.8 out of 10

Based on 15 frontline employees who took The Breakroom Quiz


Job description

Your Impact
We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a key member of our research and development efforts, you will play a crucial role in advancing the state-of-the-art in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Computer Vision and GenAI technologies for law enforcement and beyond. You will collaborate with cross-functional teams to design, develop, and deploy cutting-edge LLM, MLLM, CV models and algorithms and solutions that enable intelligent reasoning, perception and understanding of multimodal data.
What You'll Do
Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see https://www.axon.com/company)

  • US: Seattle, Boston, Scottsdale
Responsibilities
  • Own one or more key technical areas across LLM, MLLM, CV product portfolio.
  • Provide technical leadership to junior scientists, guiding the transition of R&D concepts into impactful Axon product feature.
  • Research and develop cutting-edge techniques in LLM, MLLMs, GenAI, and Computer Vision across cloud, devices and sensors based data sources.
  • Design and implement efficient and scalable MLLM models for inference and analysis of multimodal data.
  • Explore novel approaches to address challenges in NLP, NLU, Object Detection, Object Recognition, Object Tracking, Segmentation, and Scene Understanding.
  • Optimize AI models, algorithms for performance, memory footprint, and energy efficiency to meet the requirements of resource-constrained devices.
  • Join force with MLEs or firmware or hardware engineers to leverage hardware accelerators and optimize algorithms for specific hardware architectures.
  • Evaluate the performance of LLM, MLLM, CV models using real-world datasets and design experiments to validate their effectiveness.
  • Stay up-to-date with the latest research trends and advancements in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant findings into our projects.
  • Contribute to patent disclosures, academic publications, and technical documentation to share insights and findings with the broader community.
  • Experience coach and mentor junior scientists.
What You Bring
  • PhD and with +5 years for ML Scientist, +8 years for Sr. ML Scientist, +10 years for Principal ML Scientist experience in Computer Science or a related field with a focus on LLM, MLLMs, Computer Vision, GenAI.
  • Proven track record of research excellence in LLM, MLLM, Computer Vision, Robotics Perception, GenAI, demonstrated through publications in top-tier conferences or journals.
  • Strong proficiency in programming languages such as Python, C/C++, experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras and experience with ROS or robotic operational system.
  • Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline.
  • Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale.
  • Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges
  • Experience in developing computer vision algorithms for resource-constrained devices such as mobile phones, IoT devices, or embedded systems is highly desirable.
  • Excellent problem-solving skills, analytical thinking, and the ability to work independently as well as collaboratively in a team environment.
  • Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical audiences.
Benefits that Benefit You
  • Competitive salary and 401k with employer match
  • Discretionary paid time off
  • Paid parental leave for all
  • Medical, Dental, Vision plans
  • Fitness Programs
  • Emotional & Mental Wellness support
  • Learning & Development programs
  • And yes, we have snacks in our offices

Benefits listed herein may vary depending on the nature of your employment and the location where you work

Location: This role is based out of our Scottsdale, AZ office and follows a hybrid schedule. We rely on in-person collaboration and ask that team members work onsite Tuesday through Friday, with flexibility to work remotely on Mondays. We believe connection fuels innovation, and our in-office culture is designed to support meaningful teamwork and mentorship.


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