Member of Technical Staff - RL Environments

Cohere · Londonmid

En corto

▸Construyes entornos de aprendizaje por refuerzo (RL) realistas para evaluar y entrenar agentes de IA empresarial.
▸Tu trabajo diario incluye diseñar tareas, generar datos sintéticos, verificar resultados y mejorar el rendimiento de agentes mediante iteraciones continuas.
▸Destacan los proyectos reales con impacto directo en productos de IA para empresas, con retroalimentación constante de clientes.

Proficiency in English is required for collaboration across global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia en desarrollo e optimización de agentes de IA para casos de uso específicos
✓Capacidad para analizar trayectorias de agentes y identificar puntos de fallo
✓Habilidad para crear procesos repetibles de medición de capacidades agenticas
✓Experiencia en diseño de verificadores y diseño de recompensas para RL
✓Construcción de flujos de anotación para mejorar la calidad de datos y evaluar rendimiento
✓Desarrollo de pipelines de datos sintéticos para escalar evaluaciones y entrenamiento

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Reinforcement LearningAgent evaluationSynthetic data pipelinesAnnotation workflowsVerifier implementationsReward designModel trainingHassle engineeringEvaluation systemsTask design

¿A quién escribirle en Cohere?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them. In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues. Responsibilities: There are many open problems in this space. Qualifications: You may be a good fit if: You have engineered agents and optimized them for specific industry use cases You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering You obsess over measuring agentic capabilities and turning that into a repeatable process You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs You have designed and run annotation workflows to surface insights into agent performance and verify data quality You have built synthetic data pipelines to scale eval and training efforts You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments Note: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply. We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities.

No apliques sin prepararte

Investigamos quién te entrevista, adaptamos tu CV y te ensayamos en vivo — gratis la primera.