This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a high-impact Machine Learning Scientist to join our integrated discovery team. In this role, leading from the bench, you will enable the development, validation, and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination. This role may be responsible for the management and development of individual team members. Key Responsibilities - Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties. - ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows. - Data-Driven Decision Making: Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions. - May be responsible for management and development of internal team members. Qualifications - Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field. - Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions. - 4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs. - Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly led to the achievement of program milestones. - Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines. Preferred Qualifications - Proven experience with protein-ligand co-folding models (e.g., Boltz, OpenFold, AlphaFold, etc.) and the ability to integrate these structural insights into broader ML discovery pipelines. - Expertise fine-tuning existing models with internally generated structural biology and biology data. - Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context. Skills & Competencies - A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems. - Demonstrated expertise using small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc.). - Expert level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL). - Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience. - Experience mentoring and developing teams. Equal Opportunity Statement Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.

• Conduct cutting-edge research and development in reinforcement learning and game theory. • Act as a technical expert for other researchers and engineers, guiding the research of multiple endeavors at the same time. • Collaborate with world-class researchers and engineers to bring research ideas to production. • Create prototypes targeting practical adoption in GTO Wizard. • Keep up-to-date with the latest research and development in the field by constantly reviewing scientific literature.

• Define and own what “good” means for search-augmented and agentic AI systems by designing evaluation frameworks that measure real-world quality, reliability, and user-relevant behavior beyond standard benchmarks. • Invent and validate novel evaluation methodologies for non-deterministic systems (LLMs, agents, RAG), including behavioral evals, long-tail and adversarial test sets, and task-specific metrics. • Develop rigorous statistical frameworks for model comparison, regression detection, and uncertainty estimation, ensuring evaluation results are defensible and decision-ready. • Build and maintain scalable evaluation systems—datasets, gold standards, eval harnesses, scoring pipelines, and analysis tooling—that can be reused across products and customers. • Lead customer-facing evaluation research, working directly with enterprise customers to translate domain-specific quality requirements into credible, actionable evals that support product decisions and sales outcomes. • Drive competitive evaluations and internal quality reviews, surfacing meaningful performance differences, trade-offs, and failure modes to inform product strategy and prioritization. • Partner with engineering and product teams to integrate evals into development loops, release gating, and ongoing quality monitoring. • Mentor and set standards for evaluation practice, reviewing eval designs, guiding other scientists, and shaping the long-term evals roadmap as systems become more agentic and complex. • End-to-End Project Leadership: Lead the development of new AI-driven projects, encompassing ideation, prototyping, research, infrastructure design, scalability, monitoring, and evaluation. • Rapid Iteration: Adapt quickly to user feedback and evolving requirements, ensuring continuous improvement in a fast-paced environment.

• Design and fine-tune large-scale VLMs / LLMs — and hybrid architectures — for tasks such as visual reasoning, retrieval, 3D understanding, and embodied interaction. • Build robust, efficient training and evaluation pipelines (data curation, distributed training, mixed precision, scalable fine-tuning). • Conduct in-depth analysis of model performance: ablations, bias / robustness checks, and generalisation studies. • Collaborate across research, engineering, and 3D / graphics teams to bring models from prototype to production. • Publish impactful research and help establish best practices for multimodal model adaptation.