• Design and build production-grade ML systems, including LLM-based applications and AI agents • Develop and maintain scalable data pipelines for training and evaluating machine learning models • Work with large multimodal datasets (text, images, video) and build efficient data processing workflows • Improve model quality, evaluation pipelines, and automated metrics • Implement and optimize training and inference pipelines, including GPU optimization where needed • Experiment with modern generative AI approaches, including LLMs and multimodal models • Collaborate closely with engineers and product teams to deliver robust ML solutions in production • Continuously improve system performance, reliability, and scalability

• Design, implement, train, and iterate on novel deep learning models for TCR–pMHC specificity prediction. • Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting. • Utilize scalable training infrastructure to support large-scale model development and experimentation. • Conduct rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior. • Translate biological principles of T cell recognition into principled modeling decisions. • Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains. • Provide input and technical recommendations to broader modeling discussions and roadmap planning. • Work closely with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, and aligned with overall product goals. • Communicate modeling insights, approaches, and results to cross-functional scientific audiences. • Contribute to publications, presentations, etc. through technical execution and analysis.

• Design, implement, and train novel deep learning architectures for TCR–pMHC specificity prediction. • Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting. • Leverage and influence scalable training infrastructure to support large-scale model development and experimentation. • Lead rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior. • Translate biological principles of T cell recognition into principled modeling decisions. • Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains. • Evaluate emerging ML advances and determine when and how to incorporate them into Adaptive’s modeling roadmap. • Shape the long-term technical direction of machine learning in immune receptor prediction across the organization. • Partner with computational biology, immunology, translational, and engineering teams to ensure models are capable, scalable, reproducible, and aligned with therapeutic and diagnostic goals. • Clearly communicate complex modeling insights to scientific leadership, executives, and external partners. • Contribute to intellectual property development and high-impact publications.

Summary: Join Temporal, a leading Solana native Research & Development firm, as a Machine Learning Engineer. Contribute to cutting-edge projects by designing, training, and deploying predictive models for adversarial systems while building high-performance ML infrastructure. Stay at the forefront of technological advancements by engaging with the latest research and contributing to open-source development. Responsibilities: - Design, train, and deploy predictive models for adversarial systems - Build and maintain high-performance ML infrastructure that scales from research prototypes to production systems - Stay on the frontier: read new papers, reproduce results, experiment with hardware, and contribute to open source Required Skills: - Deep foundations in optimization, statistics, and linear algebra - Expertise in ML frameworks (ideally PyTorch) and strong software engineering skills - Experience building real-time or distributed ML systems - Curiosity, independence, and a track record of original contributions (research, open source, competitions, patents) Compensation: $200,000–$300,000 base salary, equity package, and discretionary bonus