Member of Technical Staff, Research

• Research, design, and test novel, research‑specific model architectures that integrate academic literature, natural language processing (NLP), symbolic reasoning, and other methods to orchestrate the scientific process.
• Prototype and build custom tokenizers for LaTeX symbols and physical units to be treated as tokens.
• Explore alternatives to transformers through in-depth research and provide practical recommendations for model development.
• Develop reinforcement-learning loops to enable models to run independent and internal thought experiments.

• Design and automate data ingestion pipelines in collaboration with our Data Scientists & Engineers that aggregates science literature, metadata, experimental data, equations and other data sources in a robust and scalable manner.
• Establish custom benchmarks to assess the models’ understanding of physical concepts, mathematical reasoning abilities, and ability to minimize hallucinations for the benefit of scientific reliability.
• Refine and release datasets and baselines once internal tests are stable.

• Run and track model training jobs while leading the technical team through set-up, monitoring progress, and constraining costs within budget.
• Develop approaches to stage “practice runs” in a sandbox environment to develop the model’s abilities to explore ideas independently while logging results for later review.
• Develop a framework to evaluate the models’ learning using visual and statistical tools to spot patterns and blind spots.
• Add guard-rails and tests that flag poor quality model output.
• Maintain internal tools to track lists of known issues, noting failures, clear fixes, and improvements to be integrated into future development

• Work with the engineering team to ensure product feasibility and robust architecture.
• Translate technical trade-offs to non-technical stakeholders in clear terms.
• Present findings in clear updates to the technical team in order to keep the broader team appraised of progress against research milestones.

• Educational Background: PhD in physics, computer science, data science, information systems, or related field.
• Experience: Proven track record of conducting in-depth research on scientific AI models, symbolic models, machine learning or deep learning for scientific discovery. 
• Technical Skills: Familiarity with SOTA models, best practices in model development processes, in-depth AI/ML concepts, and data infrastructure.
• Collaboration & Communication:
  • Comfort working closely with engineers and other technical team members.
  • Strong written and verbal communication skills.
  • Comfortable working in a startup-style, cross-functional, remote team.
• Bonus Skills:
  • Has experience with or strong interest in physics and/or fundamental science topics.
  • Experience conducting research on AI models in an early-stage or mission-focused environment.

• Interested candidates are invited to submit their resume, a cover letter detailing their qualifications and vision for the role, and references. Please include "Member of Technical Staff, Research" in the cover letter.

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