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Model Server Assistant Jobs in San Ramon, CA (NOW HIRING)

zaimler

ML Infrastructure Engineer

San Mateo, CA · On-site

$122K - $160K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Optimize and improve the engine builder and model server that power scalable agent orchestration ... We sponsor H-1B visas and assist with immigration We value builders over résumés. If this role ...

zaimler

ML Infrastructure Engineer

San Mateo, CA · On-site

$122K - $160K/yr

Optimize and improve the engine builder and model server that power scalable agent orchestration ... We sponsor H-1B visas and assist with immigration We value builders over résumés. If this role ...

zaimler

ML Infrastructure Engineer

San Mateo, CA

$122K - $160K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Optimize and improve the engine builder and model server that power scalable agent orchestration ... We sponsor H-1B visas and assist with immigration We value builders over resumes. If this role ...

Sesame

ML Model Serving Engineer

San Francisco, CA · On-site

Experience working on high-performance server systems--you'd be just as comfortable with the ... Sesame is a voice tech startup focused on developing AI voice assistants that create natural and ...

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Model Server Assistant information

See San Ramon, CA salary details

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$15

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How much do model server assistant jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for model server assistant in San Ramon, CA is $15.00, according to ZipRecruiter salary data. Most workers in this role earn between $10.77 and $16.92 per hour, depending on experience, location, and employer.

What is a model server assistant?

Model Server Assistants are specialized software tools or agents designed to help manage, deploy, and maintain machine learning models on server infrastructure. They facilitate tasks such as model versioning, scaling, monitoring, and providing APIs for real-time inference. By automating these processes, Model Server Assistants make it easier for organizations to integrate machine learning models into production environments and ensure they run efficiently and reliably.

How does a model server assistant typically collaborate with data scientists and engineers on machine learning projects?

As a Model Server Assistant, you will work closely with data scientists to ensure machine learning models are properly deployed, monitored, and maintained in production environments. Your responsibilities often include updating model versions, troubleshooting deployment issues, and optimizing server performance. You’ll also collaborate with engineers to integrate models into existing systems and automate workflows, making strong communication and teamwork skills essential. This cross-functional collaboration not only helps you learn from experienced professionals but also opens up opportunities for growth into more specialized roles in machine learning operations.

What are the key skills and qualifications needed to thrive as a model server assistant, and why are they important?

To thrive as a Model Server Assistant, you need a strong understanding of machine learning models, server deployment, and basic programming skills, usually supported by a degree in computer science or a related field. Familiarity with tools like Docker, TensorFlow Serving, Kubernetes, and cloud platforms is typically required. Attention to detail, effective communication, and problem-solving abilities help set candidates apart in this role. These skills ensure efficient deployment, reliable model performance, and smooth collaboration between data science and engineering teams.

What is the difference between Model Server Assistant vs Model Trainer?

AspectModel Server AssistantModel Trainer
CredentialsTypically requires basic technical certifications or training in AI/ML support rolesOften requires advanced degrees or certifications in machine learning or data science
Work EnvironmentSupports model deployment and server management in data centers or cloud environmentsFocuses on developing and training models in labs or development environments
Employer & IndustryTech companies, AI service providers, cloud platformsResearch institutions, AI startups, tech firms

The Model Server Assistant primarily supports the deployment and maintenance of AI models on servers, ensuring smooth operation. In contrast, the Model Trainer focuses on developing and training models from scratch. While both roles require technical knowledge, the Model Server Assistant emphasizes support and management, whereas the Model Trainer emphasizes development and experimentation.

Do model server assistants make a lot of money?

Model server assistants typically earn entry-level wages that are below average for many jobs, with pay often ranging from minimum wage to moderate hourly rates depending on the employer and location. The role usually involves supporting model shoots or events and may require basic skills or certifications, but it is not generally considered a high-paying position.

What cities near San Ramon, CA are hiring for Model Server Assistant jobs?

Cities near San Ramon, CA with the most Model Server Assistant job openings:

Model Server Assistant jobs near you

ML Infrastructure Engineer

zaimler

San Mateo, CA • On-site

$122K - $160K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 2 days ago

Job description

About zaimler
AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it.
zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision at scale. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve.
zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're growing and deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of enterprise AI runs on, we'd love to talk.
About the Role
You'll own our inference and model-serving infrastructure end to end. This isn't a research role. It's a build role: you're setting up and scaling the systems that let our agents actually run in production, fast and reliably, at increasing concurrency.
You report to Sofus and work closely with our ML and infra teams.
What You'll Own
  • Set up and scale inference/Ray Serve for ML and LLM model serving, integrated with our data analysis and agent workflows
  • Scale agent GPU infrastructure for concurrency and efficiency across multiple agent workloads
  • Optimize and improve the engine builder and model server that power scalable agent orchestration

What You Need
  • Proven ability to build scalable ML/AI platforms from scratch, end-to-end, for production use cases. You've owned a zero-to-one build before, or can show you're capable of it
  • Deep understanding of the inference stack: vLLM, KV cache, and the optimization layers underneath model serving
  • Experience building distributed systems for AI/ML workloads at scale, connecting them to real product or vertical integrations
  • 3+ years of relevant experience. We care about capability, not tenure

Nice to Have
  • Ray / Ray Serve experience
  • Familiarity with AIBrix

Why Join
  • A rare chance to shape both company and product direction as an early team engineer
  • Work alongside engineers and researchers from LinkedIn, Visa, Meta, and Branch
  • Onsite culture in San Mateo, built for deep collaboration and high-velocity building
  • Full benefits (medical, dental, vision, 401k)
  • We sponsor H-1B visas and assist with immigration

We value builders over résumés. If this role excites you but you don't check every box, we still want to hear from you. zaimler is an equal opportunity employer.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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