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Ml Platform Jobs (NOW HIRING)

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

$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 ...

Avride

Software Engineer - ML Platform

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 ...

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 ...

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 ...

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 ...

techire ai

ML Platform Engineer

Sunnyvale, 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 Mateo, 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 ...

Apple

ML Platform Engineer

Sunnyvale, CA

$150K - $277K/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 ...

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 ...

techire ai

ML Platform Engineer

Fremont, 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

Alameda, 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 Francisco, 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 ...

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.

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How much do ml platform jobs pay per hour?

As of Jul 14, 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

ML Platform Engineer

Foxglove Technologies, Inc

San Francisco, CA • On-site

$183K - $310K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

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.

What we offer
  • Competitive equity grant in a Series B company.
  • Medical, dental, vision, and term life insurance coverage at 100% for employees and 75% for dependents, for U.S. full-time employees.
  • 401(k) matching up to 4%, for U.S. full-time employees.
  • 4 weeks of vacation, plus holidays and winter break.
  • All-expenses-paid company offsites 1-2× per year.
  • $300 monthly budget toward commuter benefits or building your personal workspace, depending on role/location.

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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