Senior AI Engineer

Alice's RL Environments team builds adversarial environments that the world's leading AI labs use to test and train their most capable agents. Each environment is a working replica of a real system with an attack planted inside it. A verifier decides whether the agent held up.

The environments run on AgenticVerse, our platform for building and running RL environments at scale. A large and fast-growing community of researchers works on it every day.

We're looking for a Senior AI Engineer to own those environments and the evaluation that decides which tasks are good enough to train a frontier model.

What you'll do

RL environments

• Build RL environments end to end: the replica systems, the planted attack, and the verifier that grades the run
• Own environment and verifier design for tool-use, web, and computer-use agents
• Make environments airtight: sandboxed, free of eval-awareness cues, and closed to reward hacking
• Read rollout traces, find where the agent took a shortcut, and close it

Task evaluation

• Own Alice's proprietary task evaluator, the engine that decides whether a task is ready for a frontier lab
• Make it the industry's sharpest quality bar for RL tasks: realism, threat-model accuracy, and training value
• Keep it learning: every customer review and every post-training run makes it stricter

Run infrastructure

• Scale the run pipeline: queue-driven runners on Kubernetes, per-run sandboxes, and a grader the agent can't reach
• Package environments so they drop into customer training and evaluation pipelines

Working with Research

• Work with researchers as one team, from a task idea to an environment that's ready to train on
• Turn what post-training runs reveal into fixes in the environments
• Review designs and code, and raise the engineering bar for the people around you

What we're looking for

• 6+ years as a backend engineer, building production systems
• BSc in Computer Science or a related field, preferably from a leading university
• Strong Python, and production experience with Docker, Kubernetes, and cloud infrastructure (AWS preferred)
• Experience designing distributed systems and message queues
• A track record of owning systems from design to production
• High autonomy in an undefined space. You take a loose problem and come back with working code
• Fluent written and spoken English

Advantage

• Academic background in AI or machine learning: a degree, thesis, or research
• Hands-on AI engineering: building with LLMs, agents, or evaluations
• Exposure to post-training: RL fine-tuning, reward design, or rollout analysis
• Background in security, red teaming, or trust and safety