ML and Agentic Systems Engineer

Reposted 9 Days Ago
Be an Early Applicant
Santa Clara, CA, USA
In-Office
224K-431K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
The role involves designing and implementing agentic workflows for ML, building AI-native systems, and improving model development through automation and self-improving loops.
Summary Generated by Built In

At NVIDIA, we’re not just building the future, we’re generating it! Our Cosmos team is pushing the boundaries of multimodal AI, simulation, and world models. As we enter the next phase, we are building agentic systems that can reason about, build, evaluate, and improve AI systems themselves.

We are building systems where AI doesn’t just run models but helps build them. This role is about creating the meta-layer of modern ML: the agents, tooling, pipelines, and feedback loops that make model development faster, smarter, and increasingly automated. Rather than focusing on inventing individual model architectures, you will build the systems that help models and teams improve continuously. We are looking for exceptional engineers who are passionate about the idea of AI-native software engineering: systems where agents can work with code, data, experiments, and evaluations to accelerate how machine learning gets done.

What you’ll be doing:

  • 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.

What we need to see:

  • 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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 3, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Significant experience building machine learning systems and software platforms, not only models.
  • Expert-level Python skills, with judgment around modularity, abstraction, and code health.
  • Deep familiarity with PyTorch, including debugging and extending model behavior.
  • Experience building pipelines, evaluation systems, or workflow automation for ML.
  • Strong software engineering fundamentals, including design, testing, and debugging.
  • Strong agency in LLM-based systems, including tool use and automation.
  • BS, MS or equivalent experience in Computer Science, Engineering, or a related field.
  • 12+ years of relevant software development experience.

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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