Machine Learning Engineer

Nvidia

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Quick summary

Work type: On-site
Location: Santa Clara, CA
Employment: Full-time
Posted: 20 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $226k

$163k $290k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $200,437–$251,925.

Based on 240 similar postings.

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer

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The Machine Learning Engineer will join the engineering team to drive the development, evaluation, deployment, and end-to-end lifecycle management of AI-powered systems. The role involves architecting and scaling open-source models using distributed orchestration frameworks while building automated testing frameworks and secure continuous integration pipelines. Key responsibilities include designing data pipelines, prompt-tuning models, performing deep error analysis, and managing GPU orchestration for high-throughput inference. The candidate must utilize Python, PyTorch, TensorFlow, Scikit-learn, LangChain, Hugging Face, vLLM, and SGLang. Technical requirements include proficiency with Kubernetes, Ray, Slurm, GitLab CI/CD, and PyTest. This position solves the technical challenge of deploying production-grade AI agents and models across distributed infrastructure, ensuring system resilience, performance monitoring, and efficient memory management for complex AI workloads.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

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Skills

Python PyTorch TensorFlow Scikit-learn Kubernetes Ray Slurm LangChain Hugging Face vLLM SGLang GitLab CI/CD Git Pandas NumPy PyTest GPU Orchestration

What you'll do

Architect, deploy, and scale open-source models using distributed orchestration frameworks like Kubernetes, Ray, or Slurm.
Design and build production-grade machine learning systems and data pipelines.
Prompt-tune, evaluate, and deploy AI agents and models with mechanisms to benchmark performance.
Perform deep error and gap analysis on model outputs and build analytics dashboards for stakeholders.
Implement secure continuous integration and deployment pipelines using GitLab with integrated vulnerability scanners.
Manage GPU orchestration and infrastructure-level tuning for high-throughput, low-latency AI inference workflows.
Build automated testing frameworks and test jobs for AI workloads using PyTest and mocking libraries.
Manage the full feature lifecycle from ideation to production, including architectural choices and repository management.

What we're looking for

Master’s or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
3+ years of professional experience writing production-grade, asynchronous Python with a focus on decoupled system architecture.
Deep experience building with LangChain, Hugging Face libraries, vLLM, SGLang, TensorFlow, PyTorch, and Scikit-learn.
Hands-on experience with production-grade model deployment and scaling using Kubernetes, Ray, or Slurm.
Advanced knowledge of GitLab pipelines, including automated test jobs and vulnerability scanners.
Strong understanding of GPU memory management and infrastructure-level tuning for high-throughput AI inference.
Expert familiarity with Python testing frameworks like PyTest and advanced Git workflows.
Experience with alignment/fine-tuning of LLMs and VLMs (preferred).