Software Developer | Agentic AI

• Engineer sophisticated multi-agent frameworks and advanced retrieval structures (RAG) that interface seamlessly with scientific compute environments and specialized simulation data.
• Optimize ML model training loops by designing intelligent, automated data pipelines and recursive feedback mechanisms that enhance model fidelity and training throughput.
• Formalize the automation of complex scientific workflows, utilizing code generation and autonomous agents to bridge the gap between abstract research goals and executable technical tasks.
• Deploy and maintain high-concurrency AI systems within a hybrid infrastructure of specialized supercomputing clusters and commodity hardware.
• Validate the integration of AI-driven tools through rigorous cross-functional collaboration with domain scientists to ensure technical outputs align with biophysical constraints.

This specialized search targets the nexus of Computational Biophysics and Advanced Machine Learning, requiring a candidate whose expertise transcends standard software engineering to embrace first-principles scientific discovery. The role demands an elite academic pedigree-ideally a Ph.D. or Master's in Computer Science, Bioinformatics, or a related STEM field-to bridge the gap between theoretical agentic architectures and the granular realities of atomic-scale molecular dynamics. As StaffRight Associates maneuvers this exclusive placement, we seek an individual capable of synthesizing high-level generative AI frameworks with the rigid constraints of scientific software, ensuring that emergent AI-driven agents can autonomously navigate the complexities of drug discovery and high-performance computing environments., * Architectural Philosophy: A commitment to building robust, scalable systems that treat AI not as a black box, but as a high-precision instrument for scientific inquiry.

• Engineering Depth: Expert-level mastery of Python and its ecosystem, with a proven ability to design infrastructure that survives the transition from prototype to production-scale deployment.
• Algorithmic Versatility: A profound curiosity regarding the mechanics of Generative AI, specifically in how LLM-driven agents can be harnessed for code synthesis and scientific reasoning.
• Systemic Resilience: A track record of developing tools that automate non-trivial workflows, demonstrating an ability to troubleshoot and refine complex software interactions within a research context., * Educational Foundation: A Ph.D. or Master's degree from a top-tier institution in Computer Science, Applied Mathematics, Computational Biology, or a related quantitative discipline.
• First-Principles Mastery: Strong mathematical intuition and a demonstrated ability to apply software engineering rigor to unconventional, research-heavy domains.
• Scientific Literacy (Preferred): While not mandatory, a background in the chemical or biological sciences-specifically regarding molecular simulation or pharmaceutical research-is highly advantageous.
• Technical Achievement: A portfolio of work reflecting the successful deployment of advanced software systems, preferably within an R&D or high-stakes innovation environment.