Nvidia

Nvidia

Agent Evaluation and Self-Improvement Engineer

US, CA, Santa Clara · Principal

Sponsorship not specifiedDetected 5 days ago
PythonGoRustMachine LearningData EngineeringA/B Testing

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odds of building a lasting career here

42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

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Employer immigration record

from this employer's Department of Labor filings

Green-card follow-through: 95%

Of 584 labor certifications old enough to have been used, 29 expired without the employer filing the next step. Median time from filing to decision: 497 days.97% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

Files H-1B transfers

620 transfer filings in the last year, covering 1241 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • Autonomous agents now do real work across NVIDIA, and every piece underneath them keeps changing: models, harnesses, tools, skills, and plugins.
  • Evaluation is how a team knows whether a change made an agent better or quietly made it worse.

Responsibilities

  • Build evaluation that works the same across harnesses, with automated checks on every change that catch regressions before users do.
  • Build the self-improvement loop for evolving harnesses: agents propose changes to their own skills, plugins, and harness configuration, and sandboxed evals test each one.
  • Create realistic enterprise environments: sandboxed replicas of issue trackers, code repositories, chat, and internal APIs, filled with synthetic or anonymized data, where agents safely run long, end-to-end tasks.
  • Solid grounding in experiment design:

Requirements

  • Proficiency in Python, Go, Rust, or similar Ways to stand out from the crowd:

Skills

  • Autonomous agents now do real work across
  • NVIDIA, and every piece underneath them keeps changing:
  • models, harnesses, tools, skills, and plugins.
  • Agents can already rewrite the skills and plugins they run on, then test whether the change helped.
  • Making that loop trustworthy is the hard part.
  • Come build it with us!
  • What you'll be doing:
  • Mine production traces for failures, new usage patterns, missing skills and tools, cost and latency hot spots, and successful runs.
  • Turn them into eval cases and improvement signals.

Compensation

  • The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

Benefits

  • You will also be eligible for equity and benefits.
  • Evaluate the eval datasets themselves: coverage of real usage, duplicate cases, and how closely they match production.

Company info

  • We work closely with product, platform, and hardware teams throughout NVIDIA, and we help shape open evaluation standards and benchmarks across the industry.

Equal opportunity

  • NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
  • equal opportunity employer.
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This listing is sourced directly from Nvidia's careers page and normalized into a canonical job model.