ML Infrastructure Engineer

DeepRec.ai; Menlo Park (CA); On-site; USD 250,000 - 375,000; Full time

Job summary

An innovative firm is seeking a Senior ML Infrastructure Engineer to help shape the future of machine learning systems. This role involves designing and scaling robust infrastructure for ML model development, optimizing MLOps pipelines, and collaborating with researchers and product teams. You will work on exciting challenges in applied AI and ML infrastructure while mentoring junior engineers. Join a dynamic team that values innovation and technical excellence, and be part of a company pushing the boundaries of AI research and deployment. If you're ready to make a significant impact, this is the opportunity for you!

Benefits

Competitive salary

Professional growth opportunities

Dynamic collaborative team

Innovative work environment

Qualifications

• Experience in scaling applications from 0 to 1.
• Hands-on experience in MLOps and data pipelines.

Responsibilities

• Design and scale infrastructure for ML model development and deployment.
• Optimize MLOps pipelines and collaborate with product teams.

Skills

MLOps

Cloud Computing (AWS, GCP, Azure)

Container Orchestration (Kubernetes, Docker)

System Design

Leadership Skills

Tools

AWS

GCP

Azure

Kubernetes

Docker

Job description

This range is provided by DeepRec.ai. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$250,000.00/yr - $375,000.00/yr

Senior ML Infrastructure Engineer – Applied Research Lab, Menlo Park

Are you passionate about building cutting-edge infrastructure to support AI research and deployment? Do you thrive in scaling applications from 0 to 1 and working at the intersection of research and product development? Join our Applied Research Lab in Menlo Park as a Senior ML Infrastructure Engineer and help shape the future of machine learning systems!

What You'll Do:

• Design, build, and scale robust infrastructure for ML model development, training, and deployment.
• Optimize and manage MLOps pipelines, including data pipelines, training workflows, and model-serving infrastructure.
• Work with cloud platforms (AWS, GCP, Azure) and container orchestration technologies to ensure high availability and scalability.
• Collaborate closely with researchers and product teams to bridge the gap between innovation and practical implementation.
• Mentor and guide junior engineers in best practices for system design, ML infrastructure, and MLOps methodologies.

What We’re Looking For:

• Proven experience in scaling web/desktop applications from 0 to 1.
• Strong expertise in cloud computing platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker, etc.).
• Hands-on experience in MLOps: designing and maintaining data pipelines, training workflows, and model-serving systems for cutting-edge AI models.
• Deep understanding of the research-product tension and ability to balance rapid iteration with scalable system design.
• Strong leadership skills, with experience managing and mentoring high-energy, motivated teams.

Why Join Us?

• Work on some of the most exciting challenges in applied AI and ML infrastructure.
• Be part of a dynamic, collaborative team that values innovation and technical excellence.
• Competitive salary, benefits, and opportunities for professional growth.

If you're ready to build and scale the backbone of AI research and deployment, we’d love to hear from you! Apply now to join our team in Menlo Park.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

Technology, Information and Media, Robotics Engineering, and Computer Games