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Ml Platform Jobs (NOW HIRING)

Avride

ML Platform Engineer

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

ML Platform Engineer

Austin, TX

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 ...

Sonitalent LLC

AI/ML Platform Architect

Newark, NJ · Hybrid

Job Title - AI/ML Platform Architect Job Location Newark, NJ - Onsite from day one - (May go to 3 days once onboarded) -Need to be local Duration - 6 Months+ Visa - Any Visa except H1B,CPT Rate - W2 ...

Foxglove

ML Platform Engineer

The ML Platform Engineer will design, deploy, and scale systems that support the company's data platform, focusing on the reliability and performance of the ML infrastructure used in production.

Foxglove

ML Platform Engineer

The ML Platform Engineer will design, deploy, and scale the systems that underpin Foxglove's data platform, ensuring the reliability and performance of the ML infrastructure in production.

Apple

ML Platform Engineer

Sunnyvale, CA · On-site

You will develop MLOps platforms and tools that streamline the ML development lifecycle from data ingestion to model deployment, create robust data pipelines for large-scale data collection, curation ...

Profluent

ML Platform / MLOps Engineer

Emeryville, CA

$180K - $250K/yr

ML Platform / MLOps Engineer Emeryville, California, United States; Hybrid (2-3 days on-site) Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to ...

Apple

ML Platform Engineer

Sunnyvale, CA

$147K - $272K/yr

You will develop MLOps platforms and tools that streamline the ML development lifecycle from data ingestion to model deployment, create robust data pipelines for large-scale data collection, curation ...

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 ...

Ursus, Inc.

MLOps/ML Platform Engineer

They are seeking an MLOps/ML Platform Engineer to build and operate core systems that power machine learning and AI workloads, ensuring models are fast, reliable, and cost-efficient. Responsibilities ...

Profluent

ML Platform / MLOps Engineer

Emeryville, CA · On-site +1

$200K - $330K/yr

As we continue to push the boundaries of what is possible, we're seeking an ML Platform / MLOps Engineer on the machine learning team to build and operate the infrastructure that powers our machine ...

Klaviyo

Engineering Manager, ML Platform

Boston, MA · On-site

As a Machine Learning Platform Engineering Manager , you will lead a team of engineers building the core platforms, tools, and infrastructure that ML and AI engineers rely on to create advanced ...

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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.

ML Platform Engineer

Avride

Austin, TX • On-site

Full-time

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

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

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