דרושים Senior Applied Scientist Ring Ai ב-אמזון ב-United States, Sunnyvale

You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization.You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads.Key job responsibilities
You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically exceptional with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

- 5+ years of practical work applying ML to solve complex problems experience
- Graduate degree in with specialization in artificial intelligence, natural language processing, machine learning, or computational cognitive science
- 7+ years of combined academic and research experience
- Strong publication record in top-tier journals and conferences
- Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements
- Experience programming in Java, C++, Python or related language
- Excellent written and spoken communication skills

- PhD with specialization in Artificial Intelligence, Natural Language Processing, Machine Learning, or Computational Cognitive Science
- 10+ years of combined academic and research experience
- First author research on scaling laws, evaluation metrics, data mixture optimization, data efficiency, or neural network architectures in top-tier journals and conferences
- Functional thought leader and sought after for key tech decisions
- Experience influencing decisions at an executive level
- Experience mentoring and training the research scientist community on complex technical issues
- Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
- Excellent written and spoken communication skills with successful delivery to customers

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.Key job responsibilities
- Develop AI solutions for various Prime Video Recommendation/Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods;
- Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;
- Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses;- Stay up-to-date with advancements and the latest modeling techniques in the field;
- Publish your research findings in top conferences and journals.

- PhD, or Master's degree and 6+ years of applied research experience
- 5+ 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

- 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.

Key job responsibilities

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 1+ years of building models for business application experience
- Experience programming in Java, C++, Python or related language

- PhD in computer science, machine learning, engineering, or related fields
- Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet Research publications in computer vision, deep learning or machine learning at peer-reviewed workshops, conferences or journals
- Experience with learning multi-modal LLMs and Gen AI in Computer Vision, both in the image and video domains

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.
Key job responsibilities
- Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods- Stay up-to-date with advancements and the latest modeling techniques in the field
- Publish your research findings in top conferences and journals
A day in the life

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- Experience using Unix/Linux
- Experience in professional software development

Come join us to accelerate deep learning networks on Az1/Az2/Az3 Neural Edge processors and beyond. We deliver solutions to offload Speech, Computer Vision and Generative AI workloads on a range of devices from Blink Smart Home camera to Echo Show line of products.In this role you will lead a team working alongside partner science teams to develop the compiler infrastructure and lower deep learning workloads to heterogeneous device backends. You will also drive partnerships with peer science teams to innovate on model quantization and compression techniques for efficient execution on hardware, ensuring scalability across current and future generations of ML accelerator architectures.
Key job responsibilities· Drive technical vision and strategy for the deep learning compiler stack
·. Manage project planning, execution, and delivery of compiler solutions
· Build and maintain strong partnerships with hardware, software, applied science and product teams
·. Develop and mentor team members, manage performance, and drive career growth
· Drive hiring and team building initiatives
· Architect and oversee development of software stack for deep learning accelerator
· Lead design reviews, API development, and documentation efforts
· Ensure successful delivery of deep learning workloads on heterogeneous device backends

- 3+ years of engineering team management experience
- 7+ years of working directly within engineering teams experience
- 3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience partnering with product or program management teams
- Experience with neural network inference offload to GPU, DSP or custom accelerators

- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers
- 7+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch
- Experience building and leading compiler development teams
- Experience managing teams working on application specific accelerators or custom instruction sets

Key job responsibilities
• Provide Infrastructure support of incoming system tickets, including extensive troubleshooting tasks, with responsibilities covering multiple products, features and services.
• Work on maintenance driven coding projects, primarily in Java and AWS technologies.
• Software deployment support in staging and production environments.
• Develop tools to aid operations and maintenance.
• System and Support status reporting.
• Ownership of one or more Digital products or components.
• Improve the infrastructure, operational performance, and stability.
• Participate in designs, code and procedures reviews.
• Identify opportunities arising from technical discussions and do the technical trade-offs.
• Troubleshoot, research root causes thoroughly resolve defects.
• Drive Company Wide Campaigns with Support and Engineering teams and drive it to closure.
• Keep the compliance risks for internal systems under control.
• Drive large scale projects such as migration to native, pipeline automation and drive it to closure.

- Experience in automating, deploying, and supporting infrastructure
- Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
- Experience with Linux/Unix

- Experience with CI/CD pipelines build processes

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