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Ml Platform Jobs (NOW HIRING)

Nvidia

Senior ML Platform Engineer

Santa Clara, CA

$122K - $168K/yr

We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning innovation. In this role, you will architect, build, and scale our high-performance ML infrastructure ...

Guidewire

Senior AI/ML Platform Engineer

San Mateo, CA

$119K - $163K/yr

Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring. * Design and implement infrastructure for model training ...

Afresh

Software Engineer, ML Platform

$130K - $196K/yr

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science ...

Nvidia

Senior ML Platform Engineer

Westford, MA

$108K - $149K/yr

We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning innovation. In this role, you will architect, build, and scale our high-performance ML infrastructure ...

zaimler

MLE, ML Platform

San Mateo, CA

Collaborate with ML and data engineers to ensure the platform meets research and production needs Prior Experience * PhD in CS, ML, or a related field or MS with 4+ years of relevant industry ...

Liftoff

Director, Product Management - ML Platform

$238K - $249K/yr

Liftoff is seeking a Director of Product Management, ML Platform to holistically own the product strategy and execution for our machine learning infrastructure. This is a senior, hands-on leadership ...

Liftoff

OR

$232K - $243K/yr

Liftoff is seeking a Director of Product Management, ML Platform to holistically own the product strategy and execution for our machine learning infrastructure. This is a senior, hands-on leadership ...

zaimler

MLE, ML Platform

San Mateo, CA · On-site

Collaborate with ML and data engineers to ensure the platform meets research and production needs Prior Experience * PhD in CS, ML, or a related field or MS with 4+ years of relevant industry ...

zaimler

MLE, ML Platform

San Mateo, CA

Collaborate with ML and data engineers to ensure the platform meets research and production needs Prior Experience * PhD in CS, ML, or a related field or MS with 4+ years of relevant industry ...

Afresh

Staff Software Engineer, ML Platform

$169K - $273K/yr

The ML Platform Engineering team at Afresh is responsible for building and maintaining the foundational infrastructure and tooling that powers all of our machine learning and applied science ...

zaimler

MLE, ML Platform

San Mateo, CA · On-site

Collaborate with ML and data engineers to ensure the platform meets research and production needs Prior Experience * PhD in CS, ML, or a related field or MS with 4+ years of relevant industry ...

Toyota

Senior AI/ML Platform Engineer

Plano, TX · On-site

$100K - $137K/yr

As a Senior AI/ML Platform Engineer, you will design, build, and support scalable platform capabilities that enable enterprise MLOps and LLMOps. You will work independently on features and services ...

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How much do ml platform jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for ml platform in the United States is $63.95, according to ZipRecruiter salary data. Most workers in this role earn between $50.48 and $73.80 per hour, depending on experience, location, and employer.

What is an ML Platform?

An ML (Machine Learning) Platform is a comprehensive infrastructure or set of tools that supports the end-to-end lifecycle of machine learning projects. It typically provides features for data preparation, model training, experiment tracking, deployment, and monitoring of machine learning models. ML Platforms help streamline workflows, improve collaboration among data scientists and engineers, and enable scalable and reproducible machine learning development. Popular examples include Google AI Platform, AWS SageMaker, and Azure Machine Learning.

What are the key skills and qualifications needed to thrive as an ML Platform Engineer, and why are they important?

To thrive as an ML Platform Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning concepts, and experience with cloud infrastructure, often supported by a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Kubernetes, Docker, and cloud platforms such as AWS or GCP, as well as knowledge of CI/CD systems, is typically required. Excellent problem-solving abilities, collaboration, and effective communication are vital soft skills for working across data science, engineering, and product teams. These skills ensure scalable, reliable, and efficient deployment of machine learning models, driving impactful business solutions.

What are some common challenges faced by professionals working on an ML Platform team, and how can they be addressed?

Professionals on an ML Platform team often encounter challenges such as ensuring scalability for diverse model workloads, maintaining cross-team communication, and supporting a variety of frameworks and tools. Addressing these requires strong collaboration with data scientists, software engineers, and infrastructure teams to understand their needs and pain points. Implementing clear documentation, robust monitoring, and automation can also help streamline workflows and reduce bottlenecks, making the platform more reliable and user-friendly.

What is the difference between Ml Platform vs Data Scientist?

AspectML PlatformData Scientist
Required credentialsTypically requires knowledge of cloud services, programming, and ML toolsRequires degrees in data science, statistics, or related fields, with programming skills
Work environmentPrimarily cloud-based, working with ML tools and deployment pipelinesMostly office-based, analyzing data, building models, and interpreting results
Employer and industry usageUsed by tech companies, startups, and enterprises deploying ML solutionsEmployed across industries for data analysis, modeling, and insights

ML Platform professionals focus on deploying, managing, and scaling machine learning models using cloud and software tools. Data Scientists analyze data, develop models, and interpret results. While both roles work with machine learning, ML Platform specialists handle infrastructure and deployment, whereas Data Scientists focus on data analysis and model development.

More about Ml Platform jobs
What job categories do people searching Ml Platform jobs look for? The top searched job categories for Ml Platform jobs are:
Ml Platform jobs near you
Infographic showing various Ml Platform job openings in the United States as of May 2026, with employment types broken down into 59% Full Time, and 41% Part Time. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.
Senior ML Platform Engineer

Senior ML Platform Engineer

Nvidia

Santa Clara, CA

$122K - $168K/yr

Full-time

Posted 3 days ago

Job description

NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention-the GPU-functions as the visual cortex of modern computing and is central to groundbreaking applications from generative AI to autonomous vehicles. We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning innovation.

In this role, you will architect, build, and scale our high-performance ML infrastructure using modern Infrastructure-as-Code practices. Your primary focus will be on creating reliable, automated platforms that empower scientists and engineers to train and deploy the most advanced ML models on some of the world's most powerful GPU systems. Join our top team and apply your SRE and software engineering skills to craft robust, user-friendly platforms for seamless ML development.

What You'll Be Doing:

  • Design, build, and maintain our core ML platform infrastructure as code, primarily using Ansible and Terraform, ensuring reproducibility and scalability across large-scale, distributed GPU clusters.

  • Apply SRE principles to diagnose, troubleshoot, and resolve complex system issues across the entire stack, ensuring high availability and performance for critical AI workloads.

  • Develop robust internal automation and tooling for ML workflow orchestration, resource scheduling, and platform operations, with a strong focus on software engineering best practices.

  • Collaborate with ML researchers and applied scientists to understand infrastructure needs and build solutions that streamline their end-to-end experimentation.

  • Evolve and operate our multi-cloud and hybrid (on-prem + cloud) environments, implementing monitoring, alerting, and incident response protocols.

  • Participate in on-call rotation to provide support for platform services and infrastructure running critical ML jobs, driving root cause analysis and implementing preventative measures.

  • Write high-quality, maintainable code (Python, Go) to contribute to the core orchestration platform and automate manual processes.

  • Drive the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (e.g., GB200, NVLink, etc.).

What We Need To See:

  • BS/MS in Computer Science, Engineering, or equivalent experience.

  • 5+ years in software/platform engineering or SRE roles, including 3+ years focused on ML infrastructure or distributed compute systems.

  • Strong proficiency in Infrastructure-as-Code (IaC) tools, specifically Ansible and Terraform, with a proven track record of building and managing production infrastructure.

  • SRE-oriented mindset with extensive experience in diagnosing system-level issues, performance tuning, and ensuring platform reliability.

  • Solid understanding of ML workflows and lifecycle-from data preprocessing to deployment.

  • Proficiency in operating containerized workloads with Kubernetes and Docker.

  • Strong software engineering skills in languages such as Python or Go, with a focus on automation, tooling, and writing production-grade code.

  • Experience with Linux systems internals, networking, and performance tuning at scale.

Ways To Stand Out From The Crowd:

  • Experience building or operating ML platforms supporting frameworks like PyTorch or TensorFlow at scale.

  • Deep understanding of distributed training techniques (e.g., data/model parallelism, Horovod, NCCL).

  • Expertise with modern CI/CD methodologies and GitOps practices.

  • Passion for building developer-centric platforms with great UX and strong operational reliability.

  • Proven ability to contribute code to complex orchestration or automation platforms.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 9, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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