JobgetherRemote · Netherlands
1h ago
AmazonPosted 2d ago
L4
Machine Learning Engineer, Advertising & Marketing Performance Intelligence
Machine Learning Engineer, Advertising & Marketing Performance Intelligence at Amazon scores 90 out of 100 on AI centrality, which makes it a Level 4 role on this board.
US, WA, Seattleseniorfull-time$144k-$194k
AI in this role
Build and scale production machine learning and generative AI infrastructure for automated marketing and advertising systems.
claudeanthropicmistrallangchainhugging-facebedrocksagemakerawspytorchllms
ragfine-tuningnlpmachine-learningmlopsdistributed-traininggpu-optimizationgenai
create cohesive, relevant, and truly helpful marketing experiences for every advertiser through automated processes and intelligence that enable scaled personalization. Our team is responsible for defining and publishing automated marketing communications leveraging Machine Learning, large language models (LLMs), large quantitative models (LQMs), and specialized agents using AI/ML workflows.
We are looking for a Machine Learning Engineer (MLE) to develop, deploy and scale robust ML and GenAI solutions in production environment. You will own building ML Infra for production models and feed AI/ML outputs to systems and services . In this role you will closely partner with Applied Scientists, Data Engineers,Product Managers, Software engineers to deliver and implement automated decision-making algorithms. This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, science, product management, marketing business operations and finance.
Key job responsibilities
- Collaborate with Data and Applied Scientists to process structured/unstructured data inputs, scale ML and LLM infra while optimizing Infra costs, GPU utilization, memory management, and the training workflows (like offloading optimizer states, massive parallelization, etc) for the production environments.
- Create and deliver reusable technical assets that help to accelerate the adoption of ML, Optimization and GenAI across different science initiatives
- Design and maintain production grade large-scale distributed training systems to support ML, Causal, GenAI and multi-modal foundation models.
- Optimize AWS AI/ML infra costs, GPU utilization for efficient model training, latency, costs and fine-tuning on massive datasets.
- Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows, support piloting the LLMs and identify the related issues in the system.
- Collaborate with Engineers, Data and Applied Scientists to investigate design approaches, prototype new GenAI and ML models, evaluate technical feasibility, identify and solve complex problems.
A day in the life
As a member of our team, you'll work on projects that directly impact millions of Amazon advertisers and Marketers across the globe . This role will provide exposure to state-of-the-art innovations in Big Data, AI/ML systems and help Ads Marketing automate advertiser communications with personalized and relevant content and Measure/Calibrate Marketing effectiveness using RCTs. Technologies you will have exposure to, and/or will work with, include AWS Bedrock, Agentic AI (RAG, Agentic architectures, vector databases), Amazon Q, SageMaker, Containerized deployments, Hugging Face/LangChain, Guardrail implementations and Foundational Models such as Qwen, Anthropic’s Claude / Mistral, among others.
Basic qualifications
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing
Preferred qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
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, WA, SEATTLE - 143,700.00 - 194,400.00 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
RagFine TuningNlpMachine LearningMlopsDistributed TrainingGpu OptimizationGenai
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where mlops was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Rag, Fine Tuning, Nlp, Machine Learning, and Mlops. 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.
Want your resume actually rewritten for this job?
The free preview above is everything we have today. A full resume rewrite is not live yet and has no price set. Join the waitlist and we will email you if we open it.
Similar roles
Data roles rated Level 4 at other companies.