Platform engineer, MLOps - San Francisco, CA (hybrid)

ZipRecruiter; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

An established industry player is seeking a Platform Engineer specializing in MLOps to build and maintain the infrastructure for AI/ML development. In this pivotal role, you will collaborate with machine learning engineers to implement robust CI/CD pipelines, optimize large-scale training workloads, and ensure the operational excellence of MLOps infrastructure. This position offers the opportunity to work in a dynamic environment, tackling infrastructure challenges and automating workflows, all while contributing to groundbreaking AI advancements. Join a forward-thinking company that values innovation and offers a competitive salary along with a comprehensive benefits package.

Benefits

Generous paid time off

Comprehensive medical insurance

12 weeks paid parental leave

Fertility and family planning support

Flexible spending accounts

Annual stipends for home office setup

Company-wide off-site events

Competitive salary

Stock options

401(k) plan

Qualifications

• 5+ Jahre Erfahrung in der Infrastrukturverwaltung für große Systeme.
• Expertenkenntnisse in Kubernetes, Docker und GPU-Workload-Orchestrierung.

Responsibilities

• Entwicklung und Verwaltung von CI/CD-Pipelines für maschinelles Lernen.
• Betrieb und Optimierung von Kubernetes-Clustern für GPU-Workloads.

Skills

Kubernetes

Docker

Python

Bash

Git/GitHub

Cloud Platforms (GCP, AWS, Azure)

Infrastructure as Code (Terraform)

ML Frameworks (PyTorch, Huggingface Transformers)

Monitoring Tools (Prometheus, Grafana)

Problem-solving

Job description

Job DescriptionJob Description

About Jobgether

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

One of our companies is currently looking for a Platform Engineer, MLOps in San Francisco, CA.

This role is focused on building and maintaining the infrastructure that powers AI/ML development and production environments. You’ll collaborate closely with machine learning engineers and researchers to implement robust CI/CD pipelines, oversee container orchestration systems, and optimize large-scale training and inference workloads. Your work will ensure operational excellence, improve system reliability, and scale Kubernetes clusters supporting GPU-intensive tasks. This is a high-impact position for someone passionate about automating ML workflows and solving infrastructure challenges in fast-paced, high-growth environments.

Accountabilities:

• Develop and manage CI/CD pipelines that support safe, reproducible machine learning experiments
• Set up and monitor logging, alerting, and observability systems for model training and production APIs
• Operate and optimize large Kubernetes clusters for GPU workloads
• Manage containerization using Docker and orchestrate deployments via Kubernetes
• Ensure high availability of training environments across distributed systems
• Support and enhance the performance, scalability, and security of MLOps infrastructure
• Troubleshoot complex systems and contribute to reliability improvements across ML platforms

Requirements

• 5+ years of experience building and managing core infrastructure for large-scale systems
• Deep hands-on experience with Kubernetes, Docker, and GPU workload orchestration
• Expertise in cloud platforms (GCP, AWS, or Azure) and infrastructure-as-code tools (Terraform)
• Proficiency with scripting (Python, Bash) and Git/GitHub workflows
• Familiarity with ML frameworks like PyTorch, Huggingface Transformers, TensorRT, and vLLM
• Experience with monitoring tools such as Prometheus, Grafana, or equivalent
• Strong problem-solving skills and the ability to operate in a dynamic, ambiguous environment
• Experience running inference clusters and managing CI/CD pipelines in ML-focused environments

Benefits

• Generous paid time off and company holidays
• Comprehensive medical, dental, and vision insurance for employees and dependents
• 12 weeks of paid parental leave
• Fertility and family planning support
• Early cancer detection screenings through Galleri
• Flexible spending accounts (FSA), dependent care FSA, and HSA with company contributions
• Annual stipends for home office setup, phone/internet, wellness, and learning & development
• Company-wide and team off-site events
• Competitive salary, stock options, and 401(k) plan

Jobgether hiring process disclaimer

This job is posted on behalf of one of our partner companies. If you choose to apply, your application will go through our AI-powered 3-step screening process, where we automatically select the 5 best candidates.

Our AI thoroughly analyzes every line of your CV and LinkedIn profile to assess your fit for the role, evaluating each experience in detail. When needed, our team may also conduct a manual review to ensure only the most relevant candidates are considered.

Our process is fair, unbiased, and based solely on qualifications and relevance to the job. Only the best-matching candidates will be selected for the next round.

If you are among the top 5 candidates, you will be notified within 7 days.
If you do not receive feedback after 7 days, it means you were not selected. However, if you wish, we may consider your profile for other similar opportunities that better match your experience.

Thank you for your interest!

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