Mid Level Applied AI Engineer Jobs
Mid level applied ai engineer jobs go to engineers ready to own model pipelines end to end, make architectural decisions with limited oversight, and mentor junior teammates. 39% of openings are remote or hybrid, concentrated in Technology & Software, Science & Research, and Fintech, with Amazon, Articul8, and Anthropic hiring at this level now.
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DESCRIPTION
Are you excited about leading a team of scientists to solve complex, high-impact problems using machine learning and data-driven approaches? At Amazon Advertising Sales, we are looking for an experienced Applied Science Manager who can build, grow, and guide a team that delivers research and production-quality models at scale. In this role, you will own the strategic direction of your team's scientific roadmap, partner closely with business and technical leaders, and ensure your team's work directly shapes important decisions and customer-facing products. You will bridge the gap between science, technology, and business — translating ambiguous challenges into clear research agendas and actionable results. We are specifically looking to build AI agents that can operate accurately at large scale, helping transform the way account teams operate and optimize their end-to-end workflows. We are looking for a deeply scientific leader that brings their expertise in Natural Language Processing, Large Language Models, Reinforcement Learning, Deep learning and/or Recommender Systems to bring exciting new products come to life. If you are a thoughtful, collaborative leader who thrives on turning scientific innovation into measurable impact, we want to hear from you.
Key job responsibilities
- Lead and manage a team of applied scientists and analysts, setting the strategic direction and roadmap for scientific research that influences organizational goals and annual planning processes.
- Partner with business and technical stakeholders to define vision, priorities, and success criteria — ensuring your team builds the right solutions at the right level of fidelity and that final outputs are ready to inform business decisions or move to production.
- Evaluate and improve machine learning model accuracy and performance using rigorous experimentation, feature engineering, and hyperparameter optimization, while establishing a team culture focused on reproducibility and scientific rigor.
- Hire, develop, and mentor scientists and technical contributors, providing growth opportunities and empowering team members to take ownership of key workstreams and deliver results independently.
- Identify opportunities for new analysis, efficiency improvements, and generative AI integration, allocating resources effectively and proactively mitigating risks before they become roadblocks.
A day in the life
You start your morning reviewing experiment results with your scientists, asking probing questions about model assumptions and business relevance. Mid-morning, you join a cross-functional meeting with engineering and product partners to align on priorities for an upcoming launch. After lunch, you conduct a one-on-one with a team member, coaching them on a new research proposal. Later, you draft a narrative summarizing your team's quarterly progress and outline next steps for leadership review. Throughout, you balance hands-on technical guidance with strategic planning to keep your team delivering high-quality science.
About the team
Sales AI is focused on applying scientific research and machine learning to solve real problems that matter to Amazon and its advertising customers. We value intellectual curiosity, collaboration, and a commitment to building inclusive, high-performing teams. We believe that great science happens when people with different perspectives work together toward a shared mission. As we continue to grow, you will play a key role in shaping the team's direction, expanding our capabilities, and ensuring our work has lasting impact across the business.
BASIC QUALIFICATIONS
- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of machine learning approaches and algorithms
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 with autonomous AI services and associated model development
- Experience with sequential recommendation, user intent/mission modeling, or behavioral modeling
- Experience with LLM training, fine-tuning, or adaptation (e.g., tokenizer modification, domain adaptation)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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.
LOCATION
USA, WA, SEATTLE
SALARY
183,800.00 - 248,700.00 USD annually
DESCRIPTION
Are you excited about leading a team of scientists to solve complex, high-impact problems using machine learning and data-driven approaches? At Amazon Advertising Sales, we are looking for an experienced Applied Science Manager who can build, grow, and guide a team that delivers research and production-quality models at scale. In this role, you will own the strategic direction of your team's scientific roadmap, partner closely with business and technical leaders, and ensure your team's work directly shapes important decisions and customer-facing products. You will bridge the gap between science, technology, and business — translating ambiguous challenges into clear research agendas and actionable results. We are specifically looking to build AI agents that can operate accurately at large scale, helping transform the way account teams operate and optimize their end-to-end workflows. We are looking for a deeply scientific leader that brings their expertise in Natural Language Processing, Large Language Models, Reinforcement Learning, Deep learning and/or Recommender Systems to bring exciting new products come to life. If you are a thoughtful, collaborative leader who thrives on turning scientific innovation into measurable impact, we want to hear from you.
Key job responsibilities
- Lead and manage a team of applied scientists and analysts, setting the strategic direction and roadmap for scientific research that influences organizational goals and annual planning processes.
- Partner with business and technical stakeholders to define vision, priorities, and success criteria — ensuring your team builds the right solutions at the right level of fidelity and that final outputs are ready to inform business decisions or move to production.
- Evaluate and improve machine learning model accuracy and performance using rigorous experimentation, feature engineering, and hyperparameter optimization, while establishing a team culture focused on reproducibility and scientific rigor.
- Hire, develop, and mentor scientists and technical contributors, providing growth opportunities and empowering team members to take ownership of key workstreams and deliver results independently.
- Identify opportunities for new analysis, efficiency improvements, and generative AI integration, allocating resources effectively and proactively mitigating risks before they become roadblocks.
A day in the life
You start your morning reviewing experiment results with your scientists, asking probing questions about model assumptions and business relevance. Mid-morning, you join a cross-functional meeting with engineering and product partners to align on priorities for an upcoming launch. After lunch, you conduct a one-on-one with a team member, coaching them on a new research proposal. Later, you draft a narrative summarizing your team's quarterly progress and outline next steps for leadership review. Throughout, you balance hands-on technical guidance with strategic planning to keep your team delivering high-quality science.
About the team
Sales AI is focused on applying scientific research and machine learning to solve real problems that matter to Amazon and its advertising customers. We value intellectual curiosity, collaboration, and a commitment to building inclusive, high-performing teams. We believe that great science happens when people with different perspectives work together toward a shared mission. As we continue to grow, you will play a key role in shaping the team's direction, expanding our capabilities, and ensuring our work has lasting impact across the business.
BASIC QUALIFICATIONS
- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of machine learning approaches and algorithms
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 with autonomous AI services and associated model development
- Experience with sequential recommendation, user intent/mission modeling, or behavioral modeling
- Experience with LLM training, fine-tuning, or adaptation (e.g., tokenizer modification, domain adaptation)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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.
LOCATION
USA, WA, SEATTLE
SALARY
183,800.00 - 248,700.00 USD annually
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Who's Hiring
Top Industries Hiring
- Technology & Software14
- Science & Research6
- Fintech4
- Banking & Financial Services3
- Distribution & Wholesale2
Mid Level Applied AI Engineer Jobs: Frequently Asked Questions
How do I get a mid level applied ai engineer job?
Position yourself around ownership, not just contribution. Highlight projects where you drove decisions on model selection, deployment architecture, or evaluation strategy rather than executing tasks someone else defined. Emphasize production experience, the ability to scope ambiguous problems, and any cross-functional collaboration. Applications that show measurable impact on real systems stand out over those that list tools and frameworks alone.
Which companies hire mid level applied ai engineers?
Companies hiring mid level applied ai engineers right now include Amazon, Articul8, and Anthropic, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a wide range of employers, from established technology companies scaling inference infrastructure to startups building their first production AI capabilities and enterprises integrating large language models into core workflows.
Are there remote mid level applied ai engineer jobs?
Yes, remote and hybrid options are widely available at this level. About 39% of mid level applied ai engineer openings are remote or hybrid as of November 2026, reflecting the field's strong orientation toward distributed teams. On-site roles do exist, often tied to hardware requirements, data security constraints, or team collaboration expectations at companies building foundational model infrastructure.
How do I move up to a mid level applied ai engineer role?
The move from entry level to mid level comes from accumulating ownership over time. Early-career engineers typically start executing well-defined tasks, then gradually take on features or experiments with less guidance. Building that credibility requires deepening one area such as fine-tuning, retrieval-augmented generation, or ML infrastructure, delivering projects with measurable results, and showing you can identify problems rather than just solve ones handed to you.
Which industries hire the most mid level applied ai engineers?
Mid Level applied ai engineer roles concentrate in Technology & Software, Science & Research, and Fintech, based on current listings on Migrate Mate as of November 2026. Those sectors drive hiring at this level because they combine the data scale, product complexity, and infrastructure investment that makes applied AI engineering a core function rather than an experimental initiative.