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Machine Learning Platform Engineer Jobs (NOW HIRING)

AI Chopping Block

Strong software engineering fundamentals and experience building production systems * Experience building ML infrastructure, platforms, or production machine learning systems * Experience with model ...

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Machine Learning Platform Engineer

San Francisco, CA · On-site

$245K - $345K/yr

As our next AI/ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus: * Bachelor's degree in Computer Science, Statistics ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$110K - $152K/yr

The Senior Machine Learning Platform Engineer will design and manage scalable ML infrastructure, develop cloud-based pipelines, and ensure the reliability of MLOps workflows while mentoring junior ...

Faire

Senior Staff Machine Learning Platform Engineer

$295K - $405K/yr

About this role As the Senior Staff Machine Learning Platform Engineer, you will own the technical vision and evolution of Faire's ML platform. You will set standards, influence org-wide architecture ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$112K - $154K/yr

... rigorous engineering with learning systems proven in globally deployed solutions that deliver ... Proven experience with distributed systems , cloud platforms (AWS preferred), containerization and ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$112K - $154K/yr

... rigorous engineering with learning systems proven in globally deployed solutions that deliver ... Proven experience with distributed systems , cloud platforms (AWS preferred), containerization and ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$112K - $154K/yr

... rigorous engineering with learning systems proven in globally deployed solutions that deliver ... Proven experience with distributed systems , cloud platforms (AWS preferred), containerization and ...

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Machine Learning Platform Engineer information

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How much do machine learning platform engineer jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for machine learning 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 is a machine learning platform engineer?

A Machine Learning Platform Engineer designs, builds, and maintains the infrastructure that enables machine learning development and deployment at scale. They work on areas like data pipelines, model training workflows, monitoring, and cloud or on-premises platforms to ensure ML models run efficiently in production. Their role bridges software engineering and machine learning, focusing on automation, scalability, and reliability to support data scientists and ML engineers in delivering models faster and more effectively.

What does a machine learning platform engineer do?

A typical day for a Machine Learning Platform Engineer involves designing, building, and maintaining the infrastructure that supports data science and machine learning workflows. You might spend your time developing new features for the platform, optimizing data pipelines, deploying models, and troubleshooting technical issues alongside data scientists and engineers. Collaboration is key—you’ll often work closely with cross-functional teams to understand requirements, ensure scalability, and improve the overall machine learning lifecycle. This role offers a challenging mix of software engineering and system design, so adaptability and a proactive mindset are important for success.

What skills and qualifications are needed to thrive as a machine learning platform engineer?

A Machine Learning Platform Engineer should have strong programming skills (especially in Python or Java), knowledge of machine learning frameworks (like TensorFlow or PyTorch), and experience with cloud platforms and scalable infrastructure. Familiarity with containerization tools (such as Docker and Kubernetes), CI/CD systems, and relevant certifications in cloud or machine learning technologies is highly valued. Effective problem-solving, teamwork, and clear communication are crucial soft skills for collaborating across data science and engineering teams. These capabilities enable seamless creation and maintenance of robust, high-performance machine learning platforms for scalable model development and deployment.

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Machine Learning Platform Engineer jobs near you
Infographic showing various Machine Learning Platform Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.

Machine Learning Platform Engineer

On-site

AI Chopping Block
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Re-posted 16 hours ago

Job description

About A1


There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.


Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.


About the Role


As an ML Platform Engineer, you will build the infrastructure and systems that power A1's AI capabilities.


You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.


You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.


Focus


  • Build and operate the ML infrastructure and platforms powering A1’s AI products


  • Design systems for model training, evaluation, deployment, inference, and experimentation


  • Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads


  • Improve reliability, scalability, latency, and cost efficiency of AI systems


  • Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement


  • Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster


  • Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions


  • Build production observability, monitoring, tracing, and alerting for AI/ML workloads


  • Improve AI systems across reliability, scalability, latency, throughput, and cost


  • Identify bottlenecks across the ML stack and continuously improve system performance


  • Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure


Tech Stack


  • Python


  • PyTorch / JAX


  • LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM


  • Cloud infrastructure


  • Distributed systems


  • ML/data pipelines and workflow orchestration


  • GPU infrastructure and performance tooling


  • Vector databases and retrieval infrastructure


Ideal Experience


  • Strong software engineering fundamentals and experience building production systems


  • Experience building ML infrastructure, platforms, or production machine learning systems


  • Experience with model deployment, inference, evaluation, or data pipelines


  • Strong understanding of distributed systems and system reliability


  • Ability to write clean, maintainable, production-quality code


  • Comfortable working in ambiguous, fast-moving environments


  • Bias toward ownership, experimentation, and continuous improvement


Outcomes


  • AI infrastructure reliably supports production workloads at scale


  • Models can be trained, evaluated, deployed, and improved efficiently


  • Inference systems deliver strong latency, throughput, reliability, and cost efficiency


  • ML pipelines are reproducible, observable, maintainable, and robust


  • Model and infrastructure regressions are detected quickly and diagnosed efficiently


  • Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product


  • The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge

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