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Ml Platform Engineer Jobs (NOW HIRING)

Allen Control Systems

CV/ML Platform Engineer

Austin, TX · On-site

Position Overview We are seeking an experienced CV/ML Platform Engineer with specialization in Computer Vision and Machine Learning (CV/ML) to design, build, and own the data, model, and compute ...

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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 AI/ML Platform Engineer

Plano, TX

$100K - $137K/yr

As a Senior AI/ML Platform Engineer, you will design, build, and support scalable platform capabilities that enable enterprise MLOps and LLMOps. You will work independently on features and services ...

CTC USA, LLC

MLOPS Platform Engineer

Plano, TX · On-site

Toyota Financial Services Enterprise Platforms team is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. You ...

GDIT

AI/ML Platform Engineer

Sterling, VA · Hybrid

$152K - $205K/yr

Yes AI/ML ENGINEER PRINCIPAL Own your opportunity to turn data into measurable outcomes for our customers' most complex challenges. As an AI/ML Engineer Principal at GDIT, you'll power innovation to ...

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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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What cities are hiring for Ml Platform Engineer jobs? Cities with the most Ml Platform Engineer job openings:
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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.

CV/ML Platform Engineer

Allen Control Systems

Austin, TX • On-site

Full-time

Medical, Dental, Vision, PTO

Posted 17 days ago

Job description

Company Overview
Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.
With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop have a real-world impact.
Position Overview
We are seeking an experienced CV/ML Platform Engineer with specialization in Computer Vision and Machine Learning (CV/ML) to design, build, and own the data, model, and compute infrastructure powering ACS CV/ML team. You will help manage a 130+ GPU bare-metal Kubernetes cluster, own CV/ML CI/CD pipelines, and ensure ML model training proceeds at high volume with low friction.
What You'll Do:
  • 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.

Required Technical Skills:
  • 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.

What We Offer
  • Competitive salary
  • Health, Dental, Vision Insurance
  • Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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