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Ml Platform Jobs (NOW HIRING)

techire ai

ML Platform Engineer

Sonoma, CA · On-site

$200K - $300K/yr

ML Platform Engineer / ML Infrastructure Engineer Want to build the systems that determine how quickly frontier AI research moves? The biggest bottleneck in modern AI isn't always the model--it ...

techire ai

ML Platform Engineer

Santa Clara, CA · On-site

$200K - $300K/yr

ML Platform Engineer / ML Infrastructure Engineer Want to build the systems that determine how quickly frontier AI research moves? The biggest bottleneck in modern AI isn't always the model--it ...

techire ai

ML Platform Engineer

Hayward, CA · On-site

$200K - $300K/yr

ML Platform Engineer / ML Infrastructure Engineer Want to build the systems that determine how quickly frontier AI research moves? The biggest bottleneck in modern AI isn't always the model--it ...

techire ai

ML Platform Engineer

San Jose, CA · On-site

$200K - $300K/yr

ML Platform Engineer / ML Infrastructure Engineer Want to build the systems that determine how quickly frontier AI research moves? The biggest bottleneck in modern AI isn't always the model--it ...

techire ai

ML Platform Engineer

Santa Rosa, CA · On-site

$200K - $300K/yr

ML Platform Engineer / ML Infrastructure Engineer Want to build the systems that determine how quickly frontier AI research moves? The biggest bottleneck in modern AI isn't always the model--it ...

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 ...

Inspiren

Manager, Data/ML Platform

$200K - $230K/yr

We are seeking a seasoned Senior Manager to lead our Data + ML Platform group and evolve the core platform that powers all of this. You will lead a team of Data Engineers, Data Platform Engineers ...

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How much do ml platform jobs pay per hour?

As of Jul 15, 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

ML Platform Engineer

techire ai

Sonoma, CA • On-site

$200K - $300K/yr

Other

Posted 6 days ago

Job description

ML Platform Engineer / ML Infrastructure Engineer


Want to build the systems that determine how quickly frontier AI research moves?


The biggest bottleneck in modern AI isn't always the model—it's everything around it.

Training runs that fail halfway through. GPU clusters that sit underutilised. Researchers waiting on infrastructure instead of experiments.


This team is building large-scale multimodal reasoning models and agentic AI systems, with a major focus on post-training, reinforcement learning, and eventually pre-training their own frontier models. As the research organisation grows, they're investing heavily in the platform that enables researchers to move faster.


You'll join the ML Platform team responsible for the infrastructure behind every training run, evaluation pipeline, and production deployment. Your work will directly influence how quickly researchers can experiment, iterate, and ship new capabilities.


You'll work across large-scale distributed training, GPU orchestration, ML infrastructure, and inference systems—building reliable platforms that support everything from supervised fine-tuning through to large-scale GRPO and reinforcement learning workloads.


The teams scope spans:

  • Building distributed training infrastructure for large language and multimodal models
  • Scaling GPU clusters and improving utilisation across complex workloads
  • Developing scheduling, orchestration, and fault-tolerant training systems
  • Optimising inference performance across modern serving frameworks
  • Building internal tooling that improves researcher productivity
  • Designing reliable storage, networking, and data pipelines for ML workloads


You'll collaborate daily with Research Scientists and Research Engineers, solving the engineering problems that allow frontier models to train efficiently at scale.


You’ll be one of the engineers who has already helped scale large-scale ML workloads in production. Whether your background is ML Platform, ML Infrastructure, AI Systems, LLMOps/MLOps, distributed training, inference, or GPU infrastructure, you'll understand the engineering challenges behind training and serving frontier models—and enjoy solving them.


This is a highly technical environment where engineering quality matters as much as research. The problems are difficult, the ownership is high, and your work will have a direct impact on the pace of model development.


Package

  • Location: San Francisco Bay Area or Miami hybrid
  • Salary: $200,000–$300,000 base
  • Bonus and meaningful stock
  • Well-funded company backed by over $100M, with further funding expected
  • Founded by a repeat entrepreneur with a previous billion dollar exit


If you're interested in building the infrastructure that powers the next generation of reasoning models, we'd love to hear from you.


All applicants will receive a response.

Apply