AmazonMachine Learning Engineer, Sequence Models (project Sequoia)
AI in this role
As the Machine Learning Engineer on the team, you will deliver on our engineering vision to streamline the model development lifecycle from research to production, building ML infrastructure and establishing MLOps practices that enables rapid experimentation and deployment of ML models. You will invent and design new solutions to solve complex challenges that come with petabyte scale storage.
Key job responsibilities
- Build and scale ML infrastructure across data processing, distributed training, and model serving. Optimize GPU utilization, training throughput, serving latency, and Infra costs.
- Own the data pipelines that feed model training, including ingestion of structured and unstructured inputs, schema evolution, backfills, and data quality checks across upstream sources.
- Partner with Applied Scientists to shorten the time from experiment to production.
- Evolve model serving and feature delivery to support continuous experimentation.
- Establish automated, repeatable processes for large-scale data analysis, model training, validation, and deployment.
- Own operational excellence for high-volume, low-latency production systems, including monitoring, alarming, troubleshooting, and on-call.
Basic qualifications
- 3+ years of non-internship professional software development experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Preferred qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Knowledge of machine learning model architecture and inference
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
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.
USA, NY, New York - 158,100.00 - 213,800.00 USD annually
How we rate this
Machine Learning Engineer, Sequence Models (project Sequoia) at Amazon rates 97 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.
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. Open the description and count.
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
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- 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?
- 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?
Adapt your resume
- List these exact terms on your resume: ML Ops, Computer Vision, and NLP. 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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