Research Scientist, Machine Learning

About Us

At Convergence, we're leading fundamental research in how AI integrates into our daily lives. Our research team is advancing the theoretical foundations and practical implementations of the next generation of AI agents that don't just process information but take actions, learn from experience, and collaborate with humans. By advancing large language models and vision-language models as tools for control and reasoning, we're enabling AI systems to interpret complex contexts in order to perform actions in dynamic environments, make structured decisions, and interact meaningfully with humans in real time.

We're committed to pushing the boundaries of what's possible in reinforcement learning, transformer architectures, and vision-language models. Join our research team in shaping the future of human-AI collaboration and contribute to foundational AI research that will transform the field.

The Role

We are looking for Research Scientists with strong academic backgrounds to join our team and lead research initiatives on training foundation models which power Proxy, our generalist agent.

You will work with a small team of hands-on researchers — equipped with substantial GPU resources – to advance the state-of-the-art in multi-modal vision-language models, reinforcement learning, and action models. You will develop novel architectures and training approaches while publishing your findings in top-tier AI conferences and journals.

Responsibilities

Your role will span both theoretical research and practical implementation:

• Designing and implementing novel supervised fine-tuning, preference learning and reinforcement learning techniques

• Developing innovative methods for data curation, including synthetic data generation pipelines and optimal dataset composition strategies

• Conducting rigorous experimentation to optimize model performance through data mixes, regularization techniques, and low-level optimizations

• Advancing the theoretical understanding of transformer architectures and their applications to multi-modal learning

• Publishing research findings in top-tier venues such as NeurIPS, ICML, and ICLR

Requirements

• PhD in Computer Science, Machine Learning, or related field

• Strong publication record in top-tier ML conferences (NeurIPS, ICML, ICLR) or top journals

• Demonstrated expertise in reinforcement learning

• Deep knowledge of transformer architectures and vision-language models

• Experience training large language models using techniques such as distillation and supervised fine-tuning

• Proven track record in large-scale distributed training and inference

• Strong theoretical understanding of deep learning fundamentals

• Proficiency in PyTorch and related research frameworks

Bonus Qualifications

• Postdoctoral research experience at leading academic or industrial research labs

• First-author publications specifically focused on multi-modal learning or foundation models

• Expertise in training and fine-tuning open source models

• Contributions to open-source ML frameworks

• Experience developing novel datasets or data generation approaches

• Background in causal reasoning or alignment research

Why Join Us?

• Lead fundamental research that advances the field of AI

• Publish your work in top academic venues with full company support

• Access to significant computational resources for ambitious experiments

• Collaborate with leading researchers and engineers in the field

• Opportunities to translate theoretical advances into practical applications

• Competitive compensation package and research budget