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Full Time Llm Trainer Jobs (NOW HIRING)

USA Clinics Group

LLM Engineer

Northbrook, IL · On-site

$85K - $115K/yr

Schedule: Full-time, Monday through Friday on-site. * Compensation: $85,000 - $115,000 Key ... Lead AI education, training, and change management initiatives to increase organizational adoption ...

USA Clinics Group

LLM Engineer

Northbrook, IL

$85K - $115K/yr

Schedule: Full-time, Monday through Friday on-site. * Compensation: $85,000 - $115,000 Key ... Lead AI education, training, and change management initiatives to increase organizational adoption ...

USA Clinics Group

LLM Engineer

Northbrook, IL · On-site

$85K - $115K/yr

Schedule: Full-time, Monday through Friday on-site. * Compensation: $85,000 - $115,000 Key ... Lead AI education, training, and change management initiatives to increase organizational adoption ...

NIO

LLM Algorithmic Optimization Engineer

San Jose, CA · On-site

$143K - $186K/yr

Proficiency in Python and experience with AI-related training and inference tools such as PyTorch ... The US base salary range for this full-time position is $143,200.00 - $186,000.00. * Within the ...

NIO

LLM Algorithmic Optimization Engineer

San Jose, CA

$143K - $186K/yr

Proficiency in Python and experience with AI-related training and inference tools such as PyTorch ... The US base salary range for this full-time position is $143,200.00 - $186,000.00. * Within the ...

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Full Time Llm Trainer information

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$15

$36

$92

How much do full time llm trainer jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for full time llm trainer in the United States is $36.91, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $52.88 per hour, depending on experience, location, and employer.

What is the difference between Full Time Llm Trainer vs Part Time Llm Trainer?

AspectFull Time Llm TrainerPart Time Llm Trainer
CredentialsTypically requires a law degree, specialized training in legal education, and experience in training or teachingSame as full time, but may have less extensive experience or certifications
Work EnvironmentFull-time employment, often in law schools, legal training centers, or corporate legal departmentsPart-time roles, often freelance or contractual, with flexible hours
Employer & Industry UsageCommon in academic institutions and legal training firmsUsed by law firms, legal education providers, or online training platforms

The main difference between a Full Time Llm Trainer and a Part Time Llm Trainer lies in their employment status and work hours. Full Time Llm Trainers work on a permanent basis with a consistent schedule, often within academic or corporate settings. Part Time Llm Trainers, on the other hand, have flexible schedules and may work on a contractual basis, catering to specific courses or client needs.

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Full Time Llm Trainer jobs near you
Infographic showing various Full Time Llm Trainer job openings in the United States as of August 2026, with employment types broken down into 77% Full Time, 19% Part Time, 3% Contract, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $76,772 per year, or $36.9 per hour.

LLM Evaluation Engineering Lead (Redwood City)

DeepRec.ai

Redwood City, CA • On-site

$125K - $165K/yr

Full-time

Re-posted 2 days ago

Job description

LLM Evaluations Engineering Lead – SF Bay Area (Onsite)

Full-time / Permanent

We’re partnering with a deep‑tech AI company building autonomous, agentic systems for complex physical and real‑world environments. The team operates at the edge of what’s possible today, designing AI systems that plan, act, recover, and improve over long horizons in high‑stakes settings.

They’re hiring an LLM Evaluations Engineering Lead to own the evaluation, verification, and regression layer for agentic LLM systems running end‑to‑end workflows. This is not a metrics‑only role; you’ll be building the guardrails that determine whether the system is actually getting better.

Why this role matters

As agentic LLM systems move into long‑horizon planning and execution, evals become the bottleneck.

  • Agents are actually improving
  • Changes introduce silent regressions
  • Uncertainty is shrinking or compounding
  • success reflects real‑world outcomes, not proxy metrics

Escalating incorrect evals means downstream systems fail. This role sits directly on that fault line.

What you’ll do
  • Build eval harnesses for agentic LLM systems (offline + in‑workflow)
  • Design evals for planning, execution, recovery, and safety
  • Implement verifier‑driven scoring and regression gates
  • Turn eval failures into training signals (SFT / DPO / RL)
What they’re looking for
  • Strong experience building evaluation systems for ML models (LLMs strongly preferred)
  • Excellent software engineering fundamentals:
    • Python
    • Data pipelines
    • Test harnesses
    • Distributed execution
    • Reproducibility
  • Deep understanding of agentic failure modes, including:
    • Tool misuse
    • Hallucinated evidence
    • Reward hacking
    • Brittle formatting and schema drift
  • Ability to reason about what to measure, not just how to measure it
  • Comfortable operating between research experimentation and production systems
Why join
  • Work on frontier agentic AI systems with real‑world consequences
  • Own a foundational layer that determines system reliability and progress
  • High autonomy, strong technical peers, and meaningful equity
  • Build evals that actually matter, not academic benchmarks
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