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Ml Platform Engineer Jobs (NOW HIRING)

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 ...

Vertiv Co

AI / ML Platform Engineer

Westerville, OH

The AI / ML Platform Engineer will operate within the Digital organization and play a central role in advancing Vertiv's Operational Excellence and Customer Focus & Innovation strategic priorities by ...

Paramount

ML Platform Engineer

Burbank, CA

$130K - $195K/yr

ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco Overview We are seeking a Senior Data Scientist / Machine Learning Engineer / AI Application ...

Paramount

ML Platform Engineer

Manhattan, NY · On-site

$130K - $195K/yr

ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco Overview We are seeking a Senior Data Scientist / Machine Learning Engineer / AI Application ...

Paramount

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 Overview We are seeking a Senior Data Scientist / Machine Learning Engineer / AI Application ...

Apple

ML Platform Engineer

Sunnyvale, CA

$150K - $277K/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 ...

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.

Avride

Software Engineer - ML Platform

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 ...

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All JobsMl Platform Engineer Jobs

Ml Platform Engineer information

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How much do ml platform engineer jobs pay per hour?

As of Jul 5, 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 42% Full Time, 53% Part Time, 1% Temporary, and 4% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.
ML Platform Engineer

ML Platform Engineer

Foxglove Technologies, Inc

San Francisco, CA • On-site

$183K - $310K/yr

Full-time

Posted 3 days ago

Job description

Build the data infrastructure that powers physical AI.
Physical AI is moving from research labs into production fleets across industries. As robots scale across the real world, from factories to vehicles, to defense - every workflow from product development to deployment becomes a data problem: what happened, when, on which robot, and why?
At Foxglove, we built the unified data platform for physical AI that developer and engineering teams use to answer those questions. We help teams make vast quantities of robotics data actionable, creating the data flywheel they need to develop, test, train, deploy, and operate robots with confidence.
About the Role
We're looking for a ML Platform Engineer with deep infrastructure instincts to help design, deploy, and scale the systems that power Foxglove's data platform. This is a platform-first role: you'll own the infrastructure layer that makes ML possible in production, not just the models that run on top of it.
You'll be responsible for the reliability, scalability, and performance of the ML platform itself, from inference serving and pipeline orchestration to training infrastructure and evaluation frameworks. The problems are real and urgent: petabyte-scale multimodal robotics data, high-throughput retrieval and embedding pipelines, and the internal ML flywheel that lets our team ship fast. This is a hands-on infrastructure role, not research.
What you'll do
  • Design, deploy, and operate production inference infrastructure - including model serving, autoscaling, load balancing, and cost optimization across cloud environments
  • Own the platform architecture for embedding and retrieval pipelines that power semantic search over multimodal robotics data (image, video, point cloud, and timeseries)
  • Build and maintain the training and evaluation infrastructure that enables rapid iteration on model performance - including job orchestration, experiment tracking, and dataset versioning
  • Drive cloud infrastructure decisions (AWS/GCP) that directly impact latency, throughput, reliability, and cost at scale
  • Define platform abstractions and internal tooling that let product engineers ship ML-powered features without needing to manage infrastructure themselves
  • Evaluate, integrate, and operationalize third-party ML infrastructure components; establish clear build vs. buy frameworks for the team

What We're Looking For
  • Deep, hands-on experience owning production ML infrastructure: inference serving, model optimization (e.g., vLLM, Triton, TorchServe), orchestration, and cloud cost management
  • Strong foundation in distributed systems and cloud infrastructure (AWS/GCP) - you think in terms of system reliability, failure modes, and operational burden, not just model accuracy
  • Experience architecting and operating retrieval systems at scale, including vector databases (e.g., Pinecone, Lance, turbopuffer, pgvector) and embedding pipelines over large, heterogeneous datasets
  • A platform engineer's mindset: you build systems that other engineers depend on, and you take that responsibility seriously
  • Proven ability to operate with high ownership - you can make hard infrastructure tradeoffs independently and move fast without breaking things
  • Strong communication skills; you can explain infrastructure tradeoffs clearly to both ML and non-ML engineers

Bonus Points
  • Familiarity with fine-tuning and domain adaptation techniques for LLMs or embedding models (i.e. SFT, PEFT)
  • Familiarity with data mining or hybrid search workflows, especially as applied in robotics autonomous vehicles, or physical AI workflows
  • Prior experience building ML platforms, evaluation frameworks, or data management tooling from the ground up

Why join Foxglove
  • Work on real robotics problems. Robot data is large, messy, multimodal, time-sensitive, and tied to physical-world behavior. The problems we work on span ingestion, indexing, search, visualization, replay, connectivity, collaboration, evaluation, and operations.
  • Build tools engineers rely on. Foxglove is used by robotics teams investigating failures, validating changes, reviewing field behavior, curating datasets, and operating production fleets. The work you do helps teams understand what their robots saw, what they did, and why they behaved the way they did.
  • High-leverage product surface area. A better query path, visualization workflow, Fleet connection, UI primitive, API, onboarding flow, or customer deployment can change how an entire robotics team works.
  • Ownership and autonomy. We're a small team, and people at Foxglove own meaningful work end-to-end. You'll have real influence over product direction, technical architecture, customer outcomes, and how we operate as a company.
  • Strong peers and high standards. You'll work with people who care about correctness, performance, craft, product judgment, and building software that technical users trust under pressure.
  • A mission grounded in production software. We accelerate robotics and physical AI by building the infrastructure teams use every day to connect to robots, inspect live telemetry, manage multimodal data, replay runs, investigate failures, and improve real systems.

Learn more about how we hire and work foxglove.dev/careers
Equal opportunity
Foxglove is an equal opportunity employer. We welcome candidates from different backgrounds, experiences, and communities, and we're committed to building an inclusive environment for everyone.
We encourage you to apply even if you don't meet every nice-to-have listed above. The strongest candidates often bring a mix of relevant experience, curiosity, judgment, and the ability to learn quickly.
About Foxglove
Foxglove is the data platform for Physical AI. Built for robotics teams developing real-world systems, Foxglove provides a purpose-built, modular platform to collect, organize, and learn from vast quantities of multimodal data, creating the data flywheel to safely scale from development to distributed fleets. Founded in 2021, Foxglove supports hundreds of customers across automotive, aerospace, defense, logistics, agriculture, construction, and consumer robotics to deploy the next generation of intelligent machines. Learn more at foxglove.dev.

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