Wireless Science Manager, Device Connectivity

Amazon is hiring a Wireless Science Manager, Device Connectivity in Sunnyvale, United States. It pays $211k-$286k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
The posting mentions PyTorch, TensorFlow and scikit-learn among its tools and skills.
Level classes it as a lead role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Lead an applied science team developing foundational AI and large language models for wireless connectivity and sensing solutions.

PyTorchTensorFlowscikit-learnPythonMachine learning

computer-visionnlp

Come join the Device connectivity team in building the next generation of innovative wireless solution that create a magical experience on our products and services. We actively engage in strategic initiatives, foster partnerships with industry and academia, leverage foundational artificial intelligence and large language models to stay at the forefront of the technological advancements.

We are seeking an experienced Applied Science Manager to lead and grow a team of applied scientists who are pushing the boundaries of AI/ML in wireless connectivity and sensing. In this role, you will combine deep technical expertise with strong people leadership to drive scientific innovation that directly impacts millions of customers worldwide.

Key job responsibilities
As a Applied Science Manager in the team, you will:

Build, mentor, and develop a high-performing team of applied scientists, setting the technical bar through code reviews, design reviews, and hands-on contributions while fostering a culture of scientific excellence, innovation, and operational rigor.
Define and drive the AI/ML science roadmap for wireless solutions by developing a deep understanding of Amazon's Devices and Services offerings, translating complex business problems into well-defined scientific challenges, identifying high-risk and high-impact technical directions, and guiding your team to deliver them from conception through production.
Collaborate cross-functionally with engineering, product, and business partners to drive ML development from research through optimization and onto production devices, aligning science investments with product goals while meeting on-device performance, latency, and resource constraints.
Balance exploratory research with production delivery timelines, ensuring the team maintains scientific rigor while meeting business commitments.
Represent the team's AI innovations to both internal leadership and the external scientific community through leadership reviews, publications, patents, and conference presentations, providing clear articulation of science strategy, progress, and impact.

About the team
About the team
Device Connectivity team is empowering possibilities through wireless innovation on our devices and through services, our vision is to design and develop transformative products and services that consistently exceed our customers' expectations.

Basic qualifications

- 3+ years of scientists or machine learning engineers management experience
- Knowledge of machine learning approaches and algorithms
- PhD, or Master's degree
- 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 programming in Java, C++, Python or related language
- 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

Preferred qualifications

- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in development or technical support
- Experience developing products for volume production
- Experience with conducting research in a corporate setting
- - Experience with tools such as PyTorch, TensorFlow, ONNX, TFLite, scikit-learn, numpy, scipy or edge inference frameworks

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Sunnyvale - 211,400.00 - 286,000.00 USD annually

How we rate this

Wireless Science Manager, Device Connectivity at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Wireless Science Manager, Device Connectivity is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $211k-$286k a year.
The role is based in US, CA, Sunnyvale.
The job was posted on July 16, 2026.
Seniority: lead.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionNLPPyTorchTensorFlowscikit-learnPythonMachine learning

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
What are the limits of PyTorch that you've run into, and how did you work around them?
What's a project where you used TensorFlow hands-on?
Walk me through how you've used scikit-learn in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Computer vision, NLP, PyTorch, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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