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Ml Platform Engineer Jobs (NOW HIRING)

Paramount

Senior ML Platform Engineer

Burbank, CA

$130K - $195K/yr

Job Title: Sr. ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps ...

Paramount

Senior ML Platform Engineer

Manhattan, NY · On-site

$130K - $195K/yr

Job Title: Sr. ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San Francisco We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps ...

SumerSports LLC

MLOps / ML Platform Engineer

$170K - $200K/yr

Qualifications * 4+ years of experience in ML platform, DevOps, or infrastructure engineering. * Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure)

Avride

Software Engineer - ML Platform

Austin, TX · On-site

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 ...

Toyota

Senior ML Platform Engineer

Plano, TX · On-site

$100K - $137K/yr

Who we're looking for Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Senior ML Platform Engineer . The primary responsibility of this role is to ...

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How much do ml platform engineer jobs pay per hour?

As of Sep 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 is an ML Platform Engineer?

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 skills and qualifications are needed to thrive as an ML Platform Engineer?

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.

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 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.

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Ml Platform Engineer jobs near you
Infographic showing various Ml Platform Engineer job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, 47% Part Time, and 3% Contract. Highlights an 78% Physical, 2% Hybrid, and 20% Remote job distribution, with an average salary of $133,026 per year, or $64 per hour.

CV/ML Platform Engineer

Allen Control Systems

Austin, TX • On-site

Full-time

Re-posted 19 days ago

Job description

Job Summary:
Allen Control Systems (ACS) is a cutting-edge defense startup developing an autonomous gun turret using advanced computer vision and control systems. They are seeking an experienced CV/ML Platform Engineer to design and maintain the data, model, and compute infrastructure for their CV/ML team, focusing on managing GPU clusters and CI/CD pipelines.
Responsibilities:
• Deploy and operate Kubernetes clusters on bare-metal infrastructure hosting 130+ NVIDIA GPUs, with hybrid burst capability to AWS for scalable compute and storage workloads.
• Manage NVIDIA GPU clusters for ML training.
• Own the ACS CV/ML CI/CD pipeline.
• Improve and maintain core ML infrastructure, such as model registration and versioning, experiment tracking, and model and data provenance tracking.
• Improve and maintain ML model testing, performance analysis, and reporting tools.
• Automate repetitive model training and testing tasks to increase developer velocity.
• Work with Software Team Platform Engineers to ensure efficient coordination and minimal duplication between CV/ML infrastructure and wider Software infrastructure.
• Collaborate with the Software Team to automate the optimization of models (TensorRT/quantization) for deployment on NVIDIA Jetson and other edge hardware.
Qualifications:
Required:
• 2+ years of experience in Platform Engineering or DevOps/MLOps.
• Strong programming skills are required for automating ML lifecycles and building custom CLI tools for CV engineers.
• Hands-on experience with NVIDIA GPU infrastructure, including managing CUDA libraries and development environments, GPU Operator, device plugins, and scheduling (MIG, Volcano, or fractional GPU sharing).
• Experience implementing and maintaining MLOps platforms such as Kubeflow, MLflow, Weights & Biases (W&B), or DVC for experiment tracking and model versioning.
• Familiarity with high-performance storage solutions (e.g., MinIO, WEKA, or Ceph) and data orchestration tools capable of handling terabytes of video/image data.
• Proven track record building CI/CD pipelines that include automated model validation, performance benchmarking, and artifact management for both cloud and edge targets.
• Experience with model optimization toolchains, including TensorRT, ONNX, and quantization techniques, specifically for cross-compilation to ARM targets like NVIDIA Jetson.
• Proficiency with observability stacks (ELK, Prometheus/Grafana) adapted for ML, including monitoring GPU health, training throughput, and model inference metrics.
• Strong Linux systems knowledge (Debian/Ubuntu), including networking for high-throughput data, storage, and security hardening for defense-grade production environments.
Company:
Allen Control Systems develops autonomous defense technologies designed to detect, track, and counter unmanned aerial threats. Founded in 2022, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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