Machine Learning Engineer

NVIDIA
Santa Clara
Workplace: HybridFull timeUSD 152,000 - 241,500 annuallyFunction: Data Science & Machine LearningEducation: mastersSkills: ["Ownership","Analytical thinking","Stakeholder communication","Independent execution"]

Build and scale AI-powered systems end-to-end, including architecting, deploying, and evaluating open-source models using distributed orchestration (Kubernetes, Ray, Slurm). Design ML systems and data pipelines, run benchmarks with deep error/gap analysis, and create dashboards to communicate performance. Own features from ideation to production, implement secure GitLab CI/CD with automated testing and vulnerability scanning, and optimize GPU memory for low-latency inference at scale.

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FursaFursa
NVIDIA
NVIDIA
3 days ago

Machine Learning Engineer

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Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Build and scale AI-powered systems end-to-end, including architecting, deploying, and evaluating open-source models using distributed orchestration (Kubernetes, Ray, Slurm). Design ML systems and data pipelines, run benchmarks with deep error/gap analysis, and create dashboards to communicate performance. Own features from ideation to production, implement secure GitLab CI/CD with automated testing and vulnerability scanning, and optimize GPU memory for low-latency inference at scale.
Location: Santa Clara
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Entry level

Key Responsibilities

  • •Architect, deploy, and scale open-source models using distributed orchestration frameworks (Kubernetes, Ray, Slurm) for fault-tolerant AI workloads.
  • •Design ML systems and data pipelines: prompt-tune, evaluate, and deploy production-grade models and AI agents with mechanisms to benchmark and swap models quickly.
  • •Run comprehensive benchmarks and perform error/gap analysis on model outputs, building analytics dashboards to communicate findings.
  • •Execute features from ideation to production, managing architectural choices and coordinating updates across accessible and restricted repositories.
  • •Implement secure continuous integration/deployment: build automated test frameworks and integrate vulnerability scanners into GitLab MR workflows while scaling inference across distributed infrastructure.

Pay and Benefits

Salary: USD 152,000 - 241,500 annually

Key Requirements

  • •Master’s or PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
  • •3+ years writing production-grade, asynchronous Python with decoupled, clean system architecture and design patterns.
  • •Deep experience with LangChain, Hugging Face, vLLM, and SGLang, plus ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  • •Proficient data analysis using Python (pandas, NumPy or similar) to extract insights from model evaluation results.
  • •Hands-on production model deployment, monitoring, scaling using Kubernetes, Ray, or Slurm for multi-node clusters; strong GPU memory management knowledge.
Experience:Open-sourceMachine learningAILLMsDistributed systems
Education:Master's in Computer Science, Electrical Engineering, or related field
Skills:OwnershipAnalytical thinkingStakeholder communicationIndependent execution
Tech Stack:PythonAsyncioLangChainHugging FaceVLLMSGLangTensorFlowPyTorchScikit-learnPandasNumPyKubernetesRaySlurmGitLabCI/CDPyTestMR workflowsLLM fine-tuningGPU orchestration

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
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