Description
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
Requirements
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
About Alice
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines.
In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.
Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms.
If you're creative and driven to secure the future of AI, we want to hear from you!
Summary
Build and own RL environments end to end for tool-use, web, and computer-use agents, including replica systems, planted attacks, and verifiers. Own a proprietary task evaluator and scale the run pipeline on Kubernetes. Requires 6+ years backend engineering, a BSc in CS, strong Python, and Docker, Kubernetes, and cloud experience.
Responsibilities
Build RL environments end to end (replica systems, planted attacks, verifiers); own environment and verifier design for tool-use, web, and computer-use agents; make environments airtight (sandboxed, no eval-awareness cues, closed to reward hacking); read rollout traces and close shortcuts; own proprietary task evaluator; scale run pipeline (queue-driven runners on Kubernetes, per-run sandboxes); package environments for customer pipelines; work with researchers; review designs and code
Qualifications
6+ years as a backend engineer building production systems; BSc in Computer Science or related field; strong Python; production experience with Docker, Kubernetes, and cloud infrastructure (AWS preferred); experience designing distributed systems and message queues; track record of owning systems from design to production; high autonomy in undefined spaces; fluent written and spoken English
Education requirements
BSc in Computer Science or a related field, preferably from a leading university
Experience requirements
6+ years as a backend engineer, building production systems