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Aws Machine Learning Engineer Jobs (NOW HIRING)

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift ... Robotics * Familiarity with cloud ML infrastructure (AWS, GCP). * Experience with backend ...

About the role: We're looking for an early career Machine Learning Engineer to join our team. In ... Experience with cloud platforms such as AWS, GCP, or Azure. * A strong portfolio of projects ...

... as a machine learning engineer. Must also possess: 2 years building & deploying ML-based ... AWS or GCP; 1 year of experience with Docker, TorchServe, AWS Lambda, or SageMaker; 1 year of ...

About the role: We're looking for an early career Machine Learning Engineer to join our team. In ... Experience with cloud platforms such as AWS, GCP, or Azure. * A strong portfolio of projects ...

Machine Learning Engineer

New York, NY · On-site

$160K - $250K/yr

Requires a bachelor's degree in computer science, engineering, data science or machine learning ... AWS or GCP; 1 year of experience with Docker, TorchServe, AWS Lambda, or SageMaker; 1 year of ...

About the Role We are seeking a skilled and innovative Machine Learning Engineer to join our team ... Experience with snowflake, Postgres, RDS, Redis and AWS. * Excellent problem-solving skills and ...

Applied Machine Learning Engineer | Music Software (Multiple Roles open) Role: Applied Machine ... AWS cloud environment for deploying and scaling ML solutions. • Ability to preprocess and model ...

As a Machine Learning Engineer, you're a highly motivated individual with strong fundamentals in ... on Azure/AWS/GCP) and modern data ecosystems (data lakes, DBMS). * Strong debugging and ...

The Machine Learning Engineer will leverage their strong technical background and knowledge to ... Manage and deploy cloud-based ML services across major cloud computing environments, including AWS ...

Machine Learning Engineer

Reston, VA · On-site

$110 - $170/hr

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Machine Learning Engineer Department: Engineering, Research & Development Reports to: Metallurgical ... Cloud compute (AWS or Azure) and GPU‑based training * Coursework or research projects in ...

New

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Showing results 41-60

Aws Machine Learning Engineer information

See salary details

$31.5K

$128.8K

$193.5K

How much do aws machine learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for aws machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is an AWS machine learning engineer?

AWS Machine Learning Engineers are specialized professionals who design, build, deploy, and manage machine learning models using Amazon Web Services (AWS) cloud infrastructure. They leverage AWS tools and services, such as SageMaker, to create scalable and efficient machine learning solutions for businesses. Their responsibilities include data preparation, model training, optimization, deployment, and monitoring in a cloud environment. AWS Machine Learning Engineers often collaborate with data scientists, software engineers, and DevOps teams to integrate machine learning models into production systems.

What are the key skills and qualifications needed to thrive as an AWS machine learning engineer?

To thrive as an AWS Machine Learning Engineer, you need strong proficiency in machine learning algorithms, programming languages like Python, and a solid understanding of cloud architecture, typically supported by a degree in computer science or a related field. Familiarity with AWS services such as SageMaker, Lambda, and S3, as well as relevant certifications like AWS Certified Machine Learning – Specialty, is highly valuable. Strong problem-solving, collaboration, and communication skills set top performers apart in this role. These skills ensure successful design, deployment, and optimization of scalable machine learning solutions on AWS that meet business needs.

How does an AWS machine learning engineer typically collaborate with data scientists and DevOps teams?

As an AWS Machine Learning Engineer, you’ll work closely with data scientists to operationalize models, ensuring they are scalable and production-ready on AWS platforms. You’ll also frequently collaborate with DevOps teams to automate deployment pipelines, monitor model performance, and manage infrastructure using AWS services like SageMaker, Lambda, and CloudFormation. This cross-functional teamwork is essential for maintaining reliable, efficient ML workflows and for quickly resolving issues that arise in live environments.

What is the difference between Aws Machine Learning Engineer vs Data Scientist?

AspectAws Machine Learning EngineerData Scientist
CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, deployment pipelinesData analysis, modeling, research environments
Industry UsageTech, finance, healthcare using AWS for ML solutionsResearch, analytics, business intelligence
Search/Comparison IntentFocus on cloud-based ML deployment and engineeringFocus on data analysis and modeling

While both roles involve working with data and machine learning, Aws Machine Learning Engineers specialize in deploying ML models on AWS cloud platforms, focusing on infrastructure and scalable solutions. Data Scientists primarily analyze data, build models, and generate insights, often using a variety of tools and programming languages. The roles overlap in skills but differ in their primary focus and work environment.

More about Aws Machine Learning Engineer jobs

What states have the most Aws Machine Learning Engineer jobs?

States with the most job openings for Aws Machine Learning Engineer jobs include:

Infographic showing various Aws Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Advatix Inc.

San Mateo, CA • On-site

$110 - $165/hr

Other

Medical, Dental, Vision, PTO

Posted 15 days ago


Job description

Department: Information Technology, Type: Full Time

Job Title: Machine Learning Engineer / Research Engineer

Pay: $$110,000 – $165,000 Base Salary + Equity

Shift: N/A

Location: San Mateo, CA (Peninsula) – Onsite Preferred

Schedule: Full time, Permanent Role

Visa Sponsorship: Not Available

Relocation Assistance: Not Available

Role Summary

We are looking for a highly skilled Machine Learning Engineer / Research Engineer to join our founding team and help develop intelligent systems that transform how hardware and mechanical engineers design products. This is a unique opportunity to work at the intersection of cutting‑edge machine learning research and real‑world engineering applications. You'll collaborate directly with founders, engineers, and customers to design, train, deploy, and continuously improve machine learning systems that accelerate CAD workflows and hardware design. As one of the earliest ML hires, you will have significant ownership over technical direction, architecture decisions, and the long‑term evolution of our AI platform.

Key Responsibilities Machine Learning Research & Development
  • Design, train, and optimize custom deep learning models that understand CAD workflows and generate intelligent next‑step design recommendations.
  • Develop novel machine learning approaches for geometry, design, and engineering‑related datasets.
  • Evaluate emerging research in areas such as sequence modeling, geometric deep learning, representation learning, and foundation models.
Data & Model Infrastructure
  • Build and maintain scalable Python‑based training, evaluation, and experimentation pipelines.
  • Transform complex, real‑world CAD and geometry data into high‑quality training datasets and signals.
  • Implement robust offline and online evaluation frameworks to measure model performance and business impact.
Production ML Systems
  • Own the complete ML lifecycle from research and prototyping through deployment, monitoring, and optimization.
  • Architect model‑serving infrastructure and backend components that enable fast, reliable integration into CAD environments.
  • Establish best practices for experimentation, logging, model versioning, and performance monitoring.
Cross‑Functional Collaboration
  • Work closely with founders, mechanical engineers, hardware engineers, and early customers to understand workflows and translate them into ML solutions.
  • Collaborate with backend engineers on APIs, infrastructure, data models, and platform scalability.
  • Help define the long‑term strategy for applying machine learning to hardware and CAD design.
Skills & Qualifications Machine Learning Expertise
  • 4+ years of hands‑on machine learning experience in industry, research, or a combination of both.
  • Equivalent Master's or PhD research experience will be considered.
  • Demonstrated success designing, training, improving, and deploying machine learning models—not simply utilizing hosted AI APIs.
Deep Learning & Research
  • Expert‑level proficiency with PyTorch (preferred) or similar frameworks such as TensorFlow or JAX.
  • Experience implementing custom architectures, loss functions, optimization methods, and training loops.
  • Strong understanding of model evaluation, experimentation, and performance trade‑offs.
Software Engineering
  • Strong Python programming skills with experience building production‑ready systems.
  • Ability to write clean, maintainable, and well‑tested code with appropriate documentation and abstractions.
  • Experience developing scalable ML infrastructure and backend services.
Ownership & Execution
  • Proven ability to independently drive projects from concept through deployment.
  • Experience building end‑to‑end ML systems including data pipelines, experimentation frameworks, model training, deployment, and monitoring.
  • Comfortable solving ambiguous, open‑ended technical problems.
Communication & Collaboration
  • Excellent communication skills with the ability to explain technical concepts to both technical and non‑technical stakeholders.
  • Experience working cross‑functionally with engineers, product teams, researchers, and customers.
Startup Mindset
  • Thrives in fast‑paced, high‑ownership environments.
  • Comfortable wearing multiple hats across machine learning, research, backend engineering, and infrastructure.
Preferred Qualifications
  • Published research papers or meaningful open‑source contributions demonstrating novel technical work.
  • Experience with:
    • CAD systems and workflows
    • Computational geometry
    • Computer graphics
    • 3D representations
    • Robotics
    • Familiarity with cloud ML infrastructure (AWS, GCP).
    • Experience with backend frameworks such as FastAPI, Flask, or Django.
Must‑Have Requirements
  • Must be based in the United States and possess valid work authorization.
  • Strong proficiency in Python and modern deep learning frameworks (PyTorch preferred).
  • Demonstrated experience building and deploying custom machine learning models from scratch.
  • Experience designing architectures, creating training pipelines, and shipping ML features to production.
  • Minimum 4 years of relevant industry or equivalent academic experience.
Benefits & Perks
  • Competitive salary ($110,000 – $175,000)
  • Meaningful equity ownership
  • Comprehensive medical, dental, and vision insurance
  • Catered team lunches at the San Mateo office
  • Unlimited/flexible paid time off
  • High‑impact role within a YC‑backed startup
  • Direct collaboration with experienced founders and engineers
  • Significant opportunities for growth, learning, and career advancement
  • Opportunity to help define the future of AI‑powered CAD and hardware design

HRforGrowthis an extension of the Growth Catalyst Group (GCG), a partnership of companies with more than 65 years of operating experience and a history of successfully serving customers across industries and disciplines.

GCG® is one of the world’s leading providers of business transformation solutions related to supply chain and technology solutions for order fulfillment and marketing execution. We are committed to an inclusive workplace that does not discriminate against race, nationality, religion, age, marital status, physical or mental disability, sexual orientation, gender, orgender identity. We believe in diversity and encourage anyqualifiedindividual to apply. We are an EEOCEmployer.

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