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Ml Platform Jobs (NOW HIRING)

Avride

Software Engineer - ML Platform

Austin, TX · On-site

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 ...

Avride

Software Engineer - ML Platform

Austin, TX · On-site

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 ...

Guidewire Software

ML Platform Engineer

San Mateo, CA · On-site

$124K - $210K/yr

Summary As an ML Platform Engineer, you'll help build and evolve the infrastructure that enables machine learning teams to develop, deploy, and operate models efficiently at scale. You'll work ...

Guidewire Software

ML Platform Engineer

San Mateo, CA · On-site

$124K - $210K/yr

Summary As an ML Platform Engineer, you'll help build and evolve the infrastructure that enables machine learning teams to develop, deploy, and operate models efficiently at scale. You'll work ...

Daimler

AI/ML Platform Engineer

Atlanta, GA · On-site

Job Overview The AI/ML Platform Engineer is responsible for the foundational platform capabilities that power AI and machine learning delivery across Mercedes-Benz USA. This role designs, builds, 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 ...

Stitch Fix

ML Platform Engineer

$136K - $167K/yr

About the Role As an ML Platform Engineer at Stitch Fix, you will play a key role in building and maintaining the critical infrastructure that powers machine learning and AI across our organization.

Jobgether

Our partner is looking for a ML Platform Engineer based in Netherlands. This role offers the opportunity to build and evolve the infrastructure powering advanced AI products used at enterprise scale.

Hadrian Automation

ML Platform Engineer

Los Angeles, CA · On-site

$170K - $300K/yr

The Role This is an ML infrastructure role at the core of Hadrian's technology stack. While Data Science, Operations Research, Vision, and Document AI teams build models, you will own the platform ...

New

Paramount

Senior ML Platform Engineer

Burbank, CA · On-site

$130K - $195K/yr

ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps products that shape ...

Paramount

Senior ML Platform Engineer

Manhattan, NY · On-site

$130K - $195K/yr

Job Title: Sr. ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps ...

Paramount

Senior ML Platform Engineer

Burbank, CA

$130K - $195K/yr

Job Title: Sr. ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps ...

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Ml Platform information

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How much do ml platform jobs pay per hour?

As of Aug 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 August 2026, with employment types broken down into 51% Full Time, 46% Part Time, and 3% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.

Software Engineer - ML Platform

Avride

Austin, TX • On-site

Full-time

Re-posted 8 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

Apply