Machine Learning Research Engineer

The Company 

Iris Labs, Inc. is a privately held, early-stage company pioneering the use of artificial intelligence to transform how people navigate life’s challenges and wellbeing. We are creating a platform that empowers people to build a life they are proud of and fulfilled by. We bring together psychology, product, design, engineering, and machine learning to build digital experiences that are engaging, trustworthy, and genuinely helpful.   

Iris Labs is backed by Flagship Pioneering, an innovation enterprise that conceives, creates, resources, and builds companies that invent breakthrough technologies that transform the world. Flagship has created over 100 groundbreaking companies since 2000, including Moderna. 
 

The Role 

Iris Labs seeks a talented Machine Learning Research Engineer. The successful candidate will innovate, develop and apply machine learning (ML) methods to create foundational psychological tools. This position is ideal for someone with broad expertise in machine learning, reinforcement learning from human feedback, supervised fine tuning, and computational sciences who is looking to join a very dynamic and innovative environment and pioneer the next frontier of innovation, application, and human-ai interactions enabled by state-of-the-art models. A successful candidate should have a passion for building technologies that enable others to maximize their potential.   

Working at Iris Labs, you will have the opportunity to work with world-class scientists, engineers, and researchers who work on cutting-edge ai research and applications.  
 
Key Responsibilities 

• Design, develop, and deploy ML-powered applications that leverage large language models (LLMs) to address complex challenges in wellbeing. 
• Collaborate with psychological experts and translate interdisciplinary insights into computational frameworks. 
• Help advance the state of the art in context engineering, agentic AI and learning from human feedback. 
• Build scalable pipelines for data collection, labeling, training, and deployment of LLM-based systems. 
• Support the integration of ML systems into real-world applications with measurable outcomes. 
• Communicate findings to a multi-disciplinary audience. 

Professional Experience & Qualifications 

• Masters level (or above) experience in CS, ML, and AI is preferred, but can be relaxed for exceptional candidates. 
• Strong foundation in machine learning, deep learning, and natural language processing (NLP).