Qualifications

• Strong background in deep learning, reinforcement learning, or computational modeling.
• Expert-level Python programming with experience in ML frameworks like PyTorch, JAX, TensorFlow.
• Ability to translate research ideas into scalable, production-level systems.
• Experience with distributed computing and managing large-scale ML pipelines.
• Demonstrated curiosity and hands-on experimentation skills.
• Expertise in the latest AI tools to enhance productivity and code quality.

Responsibilities

• Prototype and scale experimental models such as LLMs and RL agents on large datasets.
• Build tools and pipelines for model training, evaluation, and analysis.
• Implement and iterate state-of-the-art research findings in collaboration with scientists.
• Manage the entire machine learning lifecycle from data engineering to deployment.
• Operate independently in a fast-paced, engineering-oriented environment.

Benefits

• Opportunity to work at the intersection of research and engineering.
• Exposure to cutting-edge technologies and methodologies in AI.
• Collaborative environment with access to experienced scientists and founders.
• Potential for significant impact in implementing innovative AI solutions.
• Flexible working arrangements to promote work-life balance.