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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML and Agentic Systems Engineer - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, CA, United States (Remote available) - **Salary:** $180,000.0 - $281,250.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computing Platforms, Software Debugging, Programming Tools, Identity and Access Management, Python (Programming Language), Open Source Technology, Software Engineering, Software Systems, Data Processing, Pytorch, Large Language Models, Multi-Agent Systems, Information Technology, Codebase, Machine Learning Operations, Data Pipelines, Data Generation - **Published:** August 21, 2026 - **Apply:** https://www.jofdav.com/jobs/59336242-ml-and-agentic-systems-engineer ## About the Role * Significant experience building machine learning systems and software platforms, not only models. * Expert-level Python skills, with strong judgment around modularity, abstraction boundaries, and long-term code health. * Deep familiarity with PyTorch, including the ability to debug, adapt, and extend model behavior within larger software systems. * Experience building pipelines, evaluation systems, developer tooling, or workflow automation for ML at meaningful scale. * Strong software engineering fundamentals, including system design, testing, packaging, debugging, and collaborative codebase evolution. * Strong agency in LLM-based systems, such as tool use, planning, multi-step workflows, code agents, or automation over data and experiments. * Comfort operating in fast-moving environments where ambiguous ideas must be turned into useful systems quickly. * BS, MS, or equivalent experience in Computer Science, Engineering, or a related field. * 12+ years of relevant software development experience Ways to stand out from the crowd: * You have built agent-based systems that do real work: coding, evaluation, data generation, triage, experimentation, or orchestration. * You have contributed to impactful open-source ML, Python, or developer tooling. * Background with context compression and agent memory techniques * Familiarity with agent safety and agent identity (AuthN, AuthZ, IAM) * You bring a high bar for software craftsmanship, but know how to apply it in research-adjacent environments without slowing innovation down. ## Description * Design and implement agentic workflows across the ML lifecycle, including data generation and curation, evaluation, debugging, training orchestration, and iteration. * Build AI-native systems in which models and agents can interact with codebases, tools, experiments, and environments to improve developer and researcher productivity. * Create self-improving loops where agents help generate data, surface failures, evaluate outputs, and drive better decisions across the system. * Own and evolve large-scale Python and PyTorch codebases, turning fast-moving ideas into robust, modular, reusable software. * Design and scale evaluation platforms that combine automated metrics, human feedback, and agent-driven analysis. * Build and maintain multimodal ML pipelines spanning data processing, experimentation, benchmarking, and deployment. * Integrate open-source and internal components into unified systems that enable rapid experimentation and reliable iteration. * Raise the bar on engineering excellence across the team through strong practices in testing, reproducibility, packaging, code health, and maintainability. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Nemotron: NVIDIA's open model strategy for developers](https://www.wearedevelopers.com/videos/100064-nemotron-nvidia-s-open-model-strategy-for-developers) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [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) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [A 5-Step Open-Source Setup for Agentic Engineering](https://www.wearedevelopers.com/magazine/738-a-5-step-open-source-setup-for-agentic-engineering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)