> Markdown version of [/jobs/ext/3370148-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3370148-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, United States (Remote available) - **Experience:** Experienced - **Salary:** $152,000.0 - $241,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Systems Engineering, Automation of Tests, Distributed Computing Environment, Memory Management, Fault Tolerance, Python (Programming Language), Language Modeling, NumPy, Open Source Technology, Mockito, Tensorflow, Toolchain, Pytorch, Large Language Models, Model Validation, Gitlab, Build Management, Pytest, Gitlab-ci, Git Flow, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Performance Monitor, Slurm, Machine Learning Operations, Software Version Control, Data Pipelines, Security Orchestration, Automation & Response, Vulnerability Analysis - **Published:** September 9, 2026 - **Apply:** https://startup.jobs/machine-learning-engineer-2100-nvidia-usa-9966435 ## About the Role * You have a Master's or PhD in Computer Science, Electrical Engineering, or a related field - or equivalent experience. * Python & Systems Engineering: 3+ years of professional experience writing production-grade, asynchronous Python, with a strong focus on decoupled, clean system architecture and design patterns. * AI tools & ML Frameworks: Deep experience building with LangChain, Hugging Face libraries, vLLM, and SGLang. Experience with ML frameworks like TensorFlow, PyTorch and Scikit-learn * Data analysis: Proficient in data analysis using Python (pandas, NumPy, or similar), able to extract insights from model evaluation results and communicate findings clearly to both technical and non-technical collaborators. * Deployment & Orchestration: Hands-on experience with production-grade model deployment, performance monitoring and analysis; and scaling using Kubernetes, Ray, or Slurm to manage multi-node cluster configurations. * Hardware & Scaling Optimization: Strong understanding of GPU memory management, and infrastructure-level tuning for high-throughput, low-latency AI inference workflows. * GitLab CI/CD & Security Automation: Advanced knowledge of GitLab pipelines, specifically building automated test jobs and integrating vulnerability scanners directly into the MR workflow. * Testing Toolchains: Expert familiarity with Python testing frameworks (e.g., PyTest), mocking libraries, and automated test generation frameworks for AI workloads. * Advanced Version Control: High proficiency in advanced Git workflows, including rebase strategies, cryptographic commit signing, and managing complex public/private repository mirroring. Ways to stand out from the crowd: * Experience with alignment/fine-tuning of LLMs, including regular LLMs as well as VLMs (Vision-Language Models) or any-to-text * Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience. ## Description * Architect, deploy, and scale open-source models using distributed orchestration frameworks. Examples include container orchestration platforms like Kubernetes, distributed computing frameworks such as Ray, or workload managers like Slurm. These frameworks support highly available and fault-tolerant AI workloads. * AI Systems & Data Pipelines: Design and build machine learning systems and data pipelines. Design experiments, prompt-tune, evaluate, and deploy production-grade models and AI agents, implementing flexible mechanisms to benchmark performance and swap models quickly to fit evolving use cases. * Error & Gap Analysis: Run comprehensive model benchmarks, perform deep error and gap analysis on model outputs, and build analytics dashboards to communicate system performance findings effectively to stakeholders. * Independent Execution: Take high ownership of features from ideation to production, managing architectural choices, coordinating updates across both accessible and restricted code repositories, and community interactions. ## Related Videos - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) - [pytest: Simple, rapid and fun testing with Python](https://www.wearedevelopers.com/videos/213-pytest-simple-rapid-and-fun-testing-with-python) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Agents for the Sake of Happiness](https://www.wearedevelopers.com/videos/1387-agents-for-the-sake-of-happiness) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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