Staff Engineer, AI Environments & Evaluations

Remanence; Paris (TX); Hybrid; USD 102,000 - 147,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Hybrid work

Job summary

Remanence is building the next era of enterprise AI with high-fidelity simulations and adaptive curricula to train and evaluate agents in realistic environments. You will help design reproducible systems and evaluators that ensure learning transfers to real deployments.

The team prioritizes first principles, experimentation, and rapid iteration, aiming to deliver scalable, safe, and reliable AI training loops across complex workflows.

Qualifications

• Strong engineering fundamentals with experimental judgment.
• Curiosity about model behavior and real-world deployment.
• Experience with synthetic data, world models, agents, curriculum learning, or evaluation is valuable.

Responsibilities

• Build pipelines that generate tasks, initial states, tool configurations, and verifiers; filter for validity, diversity, and difficulty.
• Develop curriculum strategies that select and generate tasks based on model failures, learning progress, and gaps in capability coverage.
• Train learned simulators from deployment traces to model user behavior, tool responses, and environment dynamics; validate fidelity.
• Build reproducible evaluation harnesses and held-out tests that detect reward exploitation, simulator shortcuts, contamination, and failures to generalize.

Skills

Engineering mindset

Experimental judgment

Curiosity about model behavior

First principles thinking

Synthetic data

World models

Agents

Curriculum learning

Evaluation

Tools

PyTorch

Transformers

vLLM

SGLang

Ray

Docker

OpenEnv

Gymnasium

Playwright

Apache Parquet

LanceDB

DVC

Job description

Remanence is building the next era of enterprise AI with high-fidelity simulations and adaptive curricula to train and evaluate agents in realistic environments. You will help design reproducible systems and evaluators that ensure learning transfers to real deployments.

The team prioritizes first principles, experimentation, and rapid iteration, aiming to deliver scalable, safe, and reliable AI training loops across complex workflows.