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Ml Platform Engineer Jobs (NOW HIRING)

Avride

ML Platform Engineer

Austin, TX

We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute ...

Avride

ML Platform Engineer

Austin, TX · On-site

We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute ...

Apple

ML Platform Engineer

Sunnyvale, CA · On-site

You will work on infrastructure, MLOps, cloud and on-device deployment systems, and data engineering platforms that support our ML development lifecycle.You will be responsible for building and ...

Apple

ML Platform Engineer

Sunnyvale, CA

$147K - $272K/yr

You will work on infrastructure, MLOps, cloud and on-device deployment systems, and data engineering platforms that support our ML development lifecycle. You will be responsible for building and ...

SumerSports LLC

MLOps / ML Platform Engineer

As an MLOps/ML Platform Engineer, you'll build and operate the core systems that power our machine learning and AI workloads across sports domains. You'll own the infrastructure that keeps our models ...

NVIDIA

Senior ML Platform Engineer

Westford, MA · On-site

$108K - $149K/yr

They are seeking a Senior ML Platform Engineer to architect, build, and scale high-performance ML infrastructure, ensuring reliable platforms for scientists and engineers to train and deploy advanced ...

NVIDIA

Senior ML Platform Engineer

Durham, NC · On-site

$101K - $138K/yr

They are seeking a Senior ML Platform Engineer to architect, build, and scale high-performance ML infrastructure, ensuring reliable platforms for scientists and engineers to train and deploy advanced ...

NVIDIA

Senior ML Platform Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

They are seeking a Senior ML Platform Engineer to architect, build, and scale high-performance ML infrastructure, ensuring reliable platforms for scientists and engineers to train and deploy advanced ...

NVIDIA

Senior ML Platform Engineer

Boulder, CO · On-site

$110K - $151K/yr

They are seeking a Senior ML Platform Engineer to architect, build, and scale high-performance ML infrastructure, ensuring reliable platforms for scientists and engineers to train and deploy advanced ...

Nvidia

Senior ML Platform Engineer

Boulder, CO · On-site

$108K - $148K/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 ...

Nvidia

OR · On-site

$104K - $143K/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 ...

NVIDIA

Senior ML Platform Engineer

Santa Clara, CA

$121K - $167K/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 ...

Nvidia Corporation

Senior ML Platform Engineer

Santa Clara, CA · On-site

$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 ...

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Ml Platform Engineer information

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How much do ml platform engineer jobs pay per hour?

As of Jun 14, 2026, the average hourly pay for ml platform engineer 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 are ML Platform Engineers?

ML Platform Engineers are specialized software engineers who design, build, and maintain the infrastructure and tools needed to support the development, deployment, and scaling of machine learning models. They bridge the gap between data science and production engineering by automating model training, monitoring, versioning, and serving. Their work enables data scientists to focus on modeling while ensuring that ML solutions are reliable, reproducible, and scalable in real-world environments.

What is the difference between Ml Platform Engineer vs Data Scientist?

AspectML Platform EngineerData Scientist
Required credentialsBachelor's/Master's in CS, Engineering, or related; experience with cloud platformsBachelor's/Master's in Statistics, Math, or CS; strong programming skills
Work environmentBuilds and maintains ML infrastructure, collaborates with engineering teamsAnalyzes data, develops models, and interprets results
Industry usageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, tech firms, data-driven organizations

ML Platform Engineers focus on developing and maintaining the infrastructure that supports machine learning models, while Data Scientists primarily analyze data and build models. Both roles often collaborate but serve different functions within the AI and data ecosystem.

How does an ML Platform Engineer typically collaborate with data scientists and software engineers within a company?

ML Platform Engineers work closely with both data scientists and software engineers to streamline the process of developing, deploying, and maintaining machine learning models. They provide the infrastructure and tools necessary for data scientists to build and experiment with models efficiently, while ensuring seamless integration with production systems managed by software engineers. Regular communication, participation in cross-functional meetings, and shared project management tools are common ways teams collaborate. This close collaboration helps to bridge the gap between research and production, ensuring robust, scalable, and reliable ML solutions.

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 a strong background in computer science, software engineering, and machine learning concepts, often supported by a degree in a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and knowledge of ML frameworks (TensorFlow, PyTorch) are commonly required. Collaboration, problem-solving, and strong communication skills help you work efficiently with data scientists, engineers, and stakeholders. These skills ensure the development, scalability, and reliability of robust ML infrastructure that empowers teams to deploy and manage models effectively.
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Ml Platform Engineer jobs near you
Infographic showing various Ml Platform Engineer job openings in the United States as of June 2026, with employment types broken down into 60% Full Time, 37% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.

ML Platform Engineer

Avride

Austin, TX

Other

Posted 13 days ago

Job description

About the team

The ML Platform team at Avride builds the infrastructure that powers large-scale ML training and data processing for autonomous driving. We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute, and production-grade tooling for the full model lifecycle.

About the role

As an ML Platform Engineer at Avride, you'll own critical pieces of the ML stack: workflow orchestration, distributed execution, resource governance, performance.You will shape how ML teams across the company run experiments and train models at scale. You will build the abstractions and services that make training workloads reliable, cost-efficient, and fast, helping ML teams run at scale on Kubernetes with strong reliability and excellent developer experience.

What you will do
  • Build and scale our ML compute platform on Kubernetes, using Argo Workflows for training, evaluation, and data processing orchestration
  • Design and implement core platform capabilities, including a Ray-based internal SDK for distributed execution, and multi-tenant resource governance - scheduling, priorities, quotas, and policy enforcement across GPU, CPU, memory, and IO
  • Improve end-to-end training throughput and platform efficiency by optimizing data access patterns, caching, and removing bottlenecks in storage, network, and resource contention
  • Work directly with ML teams to debug complex workload issues, drive root-cause analysis, and turn recurring problems into platform-level fixes
  • Evaluate, integrate and extend open-source tooling (Argo Workflows, Ray, Kubernetes ecosystem) to meet evolving platform needs
What you will need
  • Strong proficiency in Python or Go; C++ is a plus
  • Track record of designing and building scalable, maintainable systems and services
  • Experience operating production services end-to-end: APIs, reliability practices, observability
  • Deep knowledge of Kubernetes: how scheduling, resource management, controllers, and pod lifecycle actually behave under pressure
  • Solid Linux and systems debugging skills: performance investigation, networking, storage/IO
  • Ability to troubleshoot complex production issues across logs, metrics, and traces and drive them to resolution
Nice to have
  • Experience with Argo Workflows, Ray, MLflow, or comparable distributed ML tooling
  • Hands-on experience building or operating large-scale ML training systems: GPU scheduling, distributed training, training data pipelines
  • Track record of optimizing resource usage and performance in distributed environments

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