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Key job responsibilities
As a Principal Applied Scientist, you will:
• Own the technical architecture and optimization strategy for ML models deployed across Amazon's device ecosystem, from existing to yet-to-be-shipped products.
• Develop novel model architectures optimized for our custom silicon, establishing new methodologies for model compression and quantization.
• Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision, language, and audio tasks.
• Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs.
• Spend the majority of your time doing deep technical work - developing novel ML architectures, writing critical optimization code, and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains.
• Influence architecture decisions impacting future silicon generations, establish standards for model optimization, and mentor others in advanced ML techniques.
This role requires a blend of expertise at the intersection of ML and hardware optimization. You must be an expert in model training, with deep knowledge of cutting-edge architectures for vision, language, and multimodal tasks. Crucially, you need to be a specialist in hardware-aware quantization, with hands-on experience in model compression techniques like pruning and distillation. A strong background in computer architecture and familiarity with ML accelerator designs is essential, as is expertise in efficient inference algorithms and low-precision arithmetic.
Basic Qualifications:
• Advanced degree (PhD preferred) in Computer Science, Electrical Engineering, or a related technical field
• 8+ years of experience in machine learning, with a focus on model architecture design, optimization, and deployment
• Expertise in developing and deploying deep learning models for real-world applications, including vision, language, and multimodal tasks
• Strong background in computer architecture, hardware acceleration, and efficient inference algorithms
• Hands-on experience with model compression techniques such as pruning, quantization, and distillation
• Proficiency with deep learning frameworks like TensorFlow, PyTorch, or ONNX
• PhD in Computer Science, Electrical Engineering, or a related technical field
• 10+ years of experience in machine learning, with a track record of developing novel model architectures and optimization techniques
• Proven expertise in co-designing ML models and hardware accelerators for efficient on-device inference
• In-depth understanding of the latest advancements in model compression, including techniques like knowledge distillation, network pruning, and hardware-aware quantization
• Experience working on resource-constrained embedded systems and deploying ML models on edge devices
• Demonstrated ability to influence technical strategy and mentor cross-functional teams
• Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical stakeholders
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Work hard. Have fun. Make history.We are looking for an Embedded Software Development Engineer to help design, develop, and integrate our next generation devices. In this role you will work with customers, system architects, program managers and hardware engineers to implement, troubleshoot, fix kernel drivers, BSP for our next generation devices.
You will be responsible for the development of real-time embedded firmware and embedded Linux software that implements security controls for the platform.
Key job responsibilities
- Design, build, and maintain efficient, reusable C code for multimedia BSP
- Debug and troubleshoot kernel drivers and multimedia framework integration
- Develop and customize multimedia Board Support Package (BSP) and graphics
- Implement low-level embedded software for multimedia device platforms
- Develop and test software layers within Linux Kernel multimedia frameworks
- Optimize multimedia performance and resolve system integration challenges
- Maintain code quality and technical documentation for multimedia components
- Provide technical guidance on embedded multimedia software development
- Participate in multimedia-focused code and design reviews
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Embedded C/Linux development experience
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Linux driver and kernel development
- Experience with assembly language development

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You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.In this role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale solutions for never-before-solved problems.Key job responsibilities
1. Lead end-to-end delivery of complex AI/ML engagements, from strategic planning through to pre-production deployment and optimization
2. Architect and implement advanced solutions leveraging AWS's AI/ML services, with particular focus on Generative AI using Amazon Bedrock and SageMaker
3. Provide technical leadership and mentorship to junior consultants while driving best practices across delivery teams5. Drive innovation in applied AI/ML, contributing to methodologies and reusable solutions across the practice
6. Influence customer AI strategy through technical expertise and industry insights8. Provide thought leadership in internal and external engagements
9. Support pre-sales activities to provide technical expertise and review project scoping and risksAbout the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
- Strong experience in building large scale machine learning or deep learning models and in Generative AI model development
- Experience in data and machine learning engineering and cloud native technologies
- Strong experience communicating across technical and non-technical audiences
- Strong experience facilitating discussions with senior leadership regarding technical / architectural trade-offs, best practices, and risk mitigation
- Master's degree in a quantitative field such as statistics, mathematics, data science, engineering, or computer science
- Knowledge of the primary AWS services (ec2, elb, rds, route53 & s3)
- Experience with software development life cycle (sdlc) and agile/iterative methodologies
- Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet

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As a Battery System Engineer, you will engage with an experienced cross-disciplinary staff to conceive, and design innovative consumer product. You will work closely with an internal interdisciplinary team, and outside partners to drive key aspects of product definition and execution. You must be responsive, flexible, and able to succeed within an open collaborative peer environment.Key job responsibilities
In this role, you will:
1. Lead the design, development, and delivery of Li-ion battery system per performance and safety requirements
2. Drive battery development from NPI through mass production
3. Research and evaluate emerging battery technologies5. Design battery protection circuit and pack design for NPI programs include schematic design, and component selection.
6. Develop and review battery pack schematics, BOMs and layout to meet design requirements
7. Conduct system and design reviews, failure mode and effects analysis (DFMEA), and risk assessments
8. Analyze and resolve battery-related issues in production and field
9. Perform battery safety assessment and design for safety
10. Support battery certification processes (CTIA/IEEE1725)
- Bachelor's degree in electrical engineering or equivalent
- Experience in developing functional specifications, design verification plans and functional test procedures
- 5+ years of experience with battery technology development
- Experience with designing and qualifying battery components
- Master's degree in Electrical Engineering, Chemistry or equivalent preferred
- Strong EE fundamentals in electronic circuit design/development with microcontroller-based embedded systems.
- 5+ years experience with battery cell chemistry and platform design
- 5+ years experience with battery protection and management systems including protection ICs, Chargers, and Fuel gauge
- 5+ years of experience with battery product development in high volume consumer battery e.g. Cell phone/Tablet/E-reader/wearables.
- Knowledge of embedded software/firmware integration

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Key job responsibilities
What will you do?
• Analyze deep learning workloads and map them to Amazon’s Neural Edge Engine
• Propose and implement new hardware architectures or improvements to our existing ones, that enable future ML workloads to run efficiently on our accelerator
• Collaborate closely with compiler engineers, model developers, hardware architects and product teams to build the best ML centric hardware and software solutions for our devices
• Deliver hardware architecture, microarchitecture and other design collateral for our next generation ML accelerators
• Build tools for modeling and performance evaluation to enable power, performance, cost options and trade offs
• Work with full stack silicon designers to realize the architecture on silicon.
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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Key job responsibilities
- PhD, or Master's degree and 4+ years of applied research experience
- 4+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- PhD in Computer Science, Electrical Engineering, Mathematics or related field
- Strong experience with Generative Artificial Intelligence (GenAI) technologies and Large Language Models (LLMs)
- Experience with patents or publications at top-tier peer-reviewed conferences or journals
- Experience with popular deep learning frameworks, including PyTorch

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The Role:Key job responsibilities
- Determine the applicable regulations, standards, and safety requirements for new and existing products.
- Communicate technical safety and compliance requirements to design teams, operations, and contract manufacturers.
- Participate in electrical and mechanical system design reviews to ensure that safety mitigations are properly implemented.- Direct compliance testing and certification at external labs.
- Review compliance evaluations for accuracy.
- Contribute to international expansion and certification strategies.
- Oversee all aspects of hardware compliance for a device when required.
- Stay abreast of industry activities by participating in standards development committees or other industry organizations relevant to Lab126.
A day in the life
A typical day in this role involves working with cross-functional and outside partners on regulatory compliance initiatives, connecting with our product development team to drive key aspects of product definition, compliance requirement, testing execution and deep diving safety and other compliance issues. You must be responsive, flexible and able to succeed within an openly collaborative peer environment.
- 5+ years of hardware design and validation of components, subsystems and systems experience
- Knowledge of mechanical or electrical systems design
- Experience handling product compliance, submitting products for global approvals, and obtaining certifications
- Experience with the application of U.S. and international product safety standards (e.g. UL 62368, IEC 60601) and other EN, ANSI, CSA, ISO standards.
- Experience evaluating the compliance of power supplies, direct-plug-ins, wearables, lasers, lighting powers, or toys.
- Knowledge and experience conducting safety compliance tests for US and international standards.
- Bachelor's degree in engineering, electrical engineering, systems engineering, or related technical field.
- Master’s or PhD degree in a relevant field.
- Experience performing the safety compliance assessment of devices in various categories (e.g. construction review, test plan creation, test execution, certification)
- Experience handling consumer electronics compliance
- Experience with IEC standards such as 61010 (Laboratory Equipment); 60601 (Medical equipment safety); 60335 (Household Appliances); 62471 (Lamps / LEDs); 60825 (Lasers) & Toy safety standards
- Experience conducting product risk assessments or failure modes and effects analysis (FMEA)
- Experience responding to regulator audits
- Strong interpersonal, verbal, and written communication skills, including the ability to communicate effectively with all levels of an organization.
- Experience working with third-party labs & Asia-based contract mfrs. and test labs
- Experience communicating technical topics to interdisciplinary audiences
- Experience reviewing a design's construction for compliance.

Share
Key job responsibilities
As a Principal Applied Scientist, you will:
• Own the technical architecture and optimization strategy for ML models deployed across Amazon's device ecosystem, from existing to yet-to-be-shipped products.
• Develop novel model architectures optimized for our custom silicon, establishing new methodologies for model compression and quantization.
• Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision, language, and audio tasks.
• Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs.
• Spend the majority of your time doing deep technical work - developing novel ML architectures, writing critical optimization code, and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains.
• Influence architecture decisions impacting future silicon generations, establish standards for model optimization, and mentor others in advanced ML techniques.
This role requires a blend of expertise at the intersection of ML and hardware optimization. You must be an expert in model training, with deep knowledge of cutting-edge architectures for vision, language, and multimodal tasks. Crucially, you need to be a specialist in hardware-aware quantization, with hands-on experience in model compression techniques like pruning and distillation. A strong background in computer architecture and familiarity with ML accelerator designs is essential, as is expertise in efficient inference algorithms and low-precision arithmetic.
Basic Qualifications:
• Advanced degree (PhD preferred) in Computer Science, Electrical Engineering, or a related technical field
• 8+ years of experience in machine learning, with a focus on model architecture design, optimization, and deployment
• Expertise in developing and deploying deep learning models for real-world applications, including vision, language, and multimodal tasks
• Strong background in computer architecture, hardware acceleration, and efficient inference algorithms
• Hands-on experience with model compression techniques such as pruning, quantization, and distillation
• Proficiency with deep learning frameworks like TensorFlow, PyTorch, or ONNX
• PhD in Computer Science, Electrical Engineering, or a related technical field
• 10+ years of experience in machine learning, with a track record of developing novel model architectures and optimization techniques
• Proven expertise in co-designing ML models and hardware accelerators for efficient on-device inference
• In-depth understanding of the latest advancements in model compression, including techniques like knowledge distillation, network pruning, and hardware-aware quantization
• Experience working on resource-constrained embedded systems and deploying ML models on edge devices
• Demonstrated ability to influence technical strategy and mentor cross-functional teams
• Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical stakeholders
These jobs might be a good fit