Staff Software Engineer

Summary

Join VERSES, a cognitive computing company, as a Staff Software Engineer focused on machine learning. You will migrate research concepts into production software, design and develop scalable ML infrastructure emphasizing Bayesian inference, implement active inference agents, and optimize end-to-end ML pipelines. Responsibilities include ensuring model reliability, prototyping systems, collaborating with cross-functional teams, and participating in code reviews. This role requires 10+ years of software engineering experience (3+ years in ML), expertise in probabilistic modeling and Bayesian inference, experience with ML pipelines and deep learning frameworks, and a Bachelor's degree in Computer Science or equivalent. VERSES offers a global virtual work environment, responsible paid time off, a generous total rewards package, and a culture of highly engaged employees.

Requirements

• At least 10 years of experience in software engineering, including 3+ years in ML-focused roles
• Strong expertise in probabilistic modeling and Bayesian inference, including a solid understanding of variational inference
• Experience designing ML pipelines using tools like Airflow, Kubeflow, or MLflow
• Fluency in Python and experience with deep learning frameworks (e.g. PyTorch, TensorFlow, JAX)
• Experience with agile software development methodologies
• Bachelor's degree in Computer Science or a related field or equivalent years of work experience
• Experience with reinforcement learning or control theory frameworks
• Experience with core machine learning and AI concepts, including generative models (like GPTs), and an awareness of emerging paradigms such as active inference
• Track record of deploying ML models in high-availability systems (real-time inference, edge computing)
• Experience with probabilistic programming frameworks to solve real world inference and decision making problems
• Excellent problem-solving and analytical skills
• Strong written and verbal communication skills
• A passion and spirit of innovation

Responsibilities

• Migrate research concepts and ideologies (ie Active Inference) into production software
• Contribute to the design and development of robust, scalable, ML infrastructure with emphasis on Bayesian inference and uncertainty quantification
• Implement active inference agents and models to support adaptive, goal-directed behavior in dynamic environments
• Optimize end-to-end ML pipelines from data ingestion and preprocessing to deployment and monitoring
• Ensure observability, reproducibility, and reliability of models throughout the development lifecycle
• Rapidly prototype systems for live demonstration, applying cutting-edge technologies to connect the physical and digital worlds
• Collaborate with cross-functional teams, including data scientists, researchers, designers, and other engineers, to define software requirements and integrate probabilistic models into production systems
• Actively participate in technical discussions, offer mentorship, and ensure adherence to coding standards and best practices through active participation in code reviews
• Work in a small and tight-knit agile innovation unit running ahead of the main product team
• Develop technical documentation and participate in knowledge sharing sessions

Benefits

• Global virtual work environment (although some positions may need to operate within specific time zones)
• Responsible paid time off policy (RTO), and company-recognized Holidays
• Generous total rewards package