AmazonPosted 2w ago

Applied Scientist, Alexa Enterprise

Applied Scientist, Alexa Enterprise at Amazon scores 90 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, WA, Seattlemidfull-time$172k-$222k

AI in this role

fine-tuningai-evaluationspeech

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment.

In this role, you’ll develop and apply scientific techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You’ll work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You’ll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. The improvements inspired by one customer’s needs become capabilities that serve many.

We’re a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they’re deploying in environments where the technical challenges are real and the feedback loops are immediate.

Key job responsibilities
- Develop and apply modeling techniques to improve speech-to-text accuracy for domain-specific use cases and real-world operating conditions
- Evaluate, select, adapt, and fine-tune speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints
- Improve the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription
- Design datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases
- Optimize models and inference pipelines for reliable, real-time operation
- Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities
- Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries
- Collaborate with software engineers to productionize models, measure their performance, and continuously improve deployed systems
- Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business stakeholders
- Contribute to the team’s scientific direction, mentor teammates, participate in hiring, and improve science and engineering processes

About the team
The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes.

Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Applied Scientists on the team have real ownership, from identifying and framing customer problems through experimentation, production integration, and measurement of customer impact. This is an opportunity to solve meaningful scientific problems while helping shape a platform in its early stages.

Basic qualifications

- 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
- Knowledge of standard speech and machine learning techniques
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

- Have publications at top-tier peer-reviewed conferences or journals
- Experience applying machine learning to speech or audio processing, including generative models, speech enhancement, source separation, or related applications
- Familiarity with voice-processing techniques such as beamforming, acoustic echo cancellation, and noise reduction
- Experience optimizing speech or audio models for real-time, low-latency production systems

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 - 171,600.00 - 222,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningAI EvaluationSpeech

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you decide that one model's output is better than another's for a given task?
What have you built with speech recognition or text-to-speech, and where did it break?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Fine Tuning, AI Evaluation, and Speech. 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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