AI Research Engineer

Key Responsibilities

• Design, build, and iterate on experimental ML pipelines supporting foundational model development.
• Implement and train large-scale models, including transformers and diffusion-based architectures, for generative and control tasks.
• Develop and evaluate reinforcement learning algorithms and frameworks for autonomous behaviors.
• Rapidly prototype and deploy research ideas into working code to accelerate AI experimentation cycles.
• Collaborate with cross-functional teams to integrate ML components into real-world systems.

Minimum Qualifications

• Master’s degree in Machine Learning, AI, Computer Science, or a related technical field.
• Minimum 3 years of experience in applied ML research and software engineering.
• Hands-on experience training (not just using) large-scale ML models such as transformers or diffusion models.
• Strong understanding of reinforcement learning fundamentals and applications.
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
• Proven ability to write clean, efficient, and scalable code that supports fast iteration.
• Excellent collaboration and communication skills.
• Willing to occasionally travel.
• Must have and maintain US work authorization.

Preferred Qualifications

• PhD in Machine Learning, AI, Computer Science, or related field.
• 4+ years of experience in applied ML research and software engineering.
• Experience designing ML experimentation frameworks and model training pipelines.
• PRACTICAL knowledge of simulation environments or robotics systems.
• Familiarity with multimodal architectures and imitation learning.
• Understanding of edge compute or distributed ML training infrastructure.