Foundational LLM Researcher/Engineer (Computational Biophysics & ML)
Seeking an elite researcher-engineer with a Ph.D. or Master's to architect and deploy large-scale, multimodal LLMs for therapeutic discovery. Responsibilities include orchestrating distributed training, engineering multimodal architectures, and validating model performance. Requires deep understanding of transformer architectures, system resilience, and scientific curiosity in applying ML to physical sciences.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, StaffRight Associates, LLC, is seeking the following. Apply via Dice today!
Preface
The convergence of Computational Biophysics and Foundational Machine Learning represents the current frontier of therapeutic discovery. This mandate requires an elite practitioner possessing a Ph.D. or equivalent Master’s-level rigor, capable of applying first-principles mastery to the architectural challenges of Large Language Models (LLMs). The successful candidate will transition beyond standard NLP, utilizing deep technical intuition to bridge the gap between high-dimensional transformer architectures and the stochastic complexities of molecular dynamics. This is a forensic search for a researcher-engineer who views code as a vehicle for scientific proof and system optimization as a prerequisite for breakthrough discovery.
The Mission
StaffRight Associates is orchestrating an exclusive search for foundational LLM Researchers and Engineers to join an interdisciplinary collective dedicated to the structural alignment of generative AI and molecular science. The mission is to architect and deploy large-scale, multimodal models that transcend text, capturing the intricate spatial and temporal behaviors of biological systems at atomic resolution.
By leveraging one of the world''s most advanced specialized computing environments, the incumbent will decouple complex biochemical problems through the lens of high-performance distributed training and innovative post-training methodologies.
Core Technical Objectives
- Orchestrate large-scale distributed training and inference workflows, maximizing throughput and system efficiency on proprietary, high-performance computing (HPC) clusters.
- Synthesize robust data ingestion and preprocessing pipelines for massive-scale pre-training, ensuring data integrity across parallelized environments.
- Formalize advanced post-training protocols, including Reinforcement Learning (RLHF/RLAIF), contrastive learning, and precision instruction tuning to refine model utility.
- Engineer multimodal architectures that integrate non-linguistic data structures, specifically molecular graphs, 3D atomic coordinates, and sequential time-series data.
- Validate model performance against complex scientific benchmarks, ensuring that generative outputs align with physical and chemical constraints.
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- Architectural Philosophy: A deep-seated understanding of transformer-based architectures and the mathematical underpinnings of attention mechanisms.
- System Resilience: Proven ability to manage the volatility of large-scale training runs, possessing the "debugging intuition" required for massive parameter sets.
- Technological Versatility: A background characterized by intellectual agility—transitioning seamlessly between high-level Pythonic implementation and low-level optimization.
- Scientific Curiosity: While prior experience in drug discovery is not a prerequisite, a profound interest in applying ML to the physical sciences is essential.
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- Advanced Degree: Ph.D. or Master’s in Computer Science, Physics, Mathematics, or a related quantitative STEM field.
- Technical Stack: Expert-level command of Python; familiarity with C++/CUDA or specialized hardware acceleration is highly advantageous.
- Proven Impact: A track record of high-caliber contributions, evidenced by peer-reviewed publications at top-tier venues (e.g., NeurIPS, ICML, ICLR) or the delivery of high-impact proprietary models at leading AI research labs.
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- Base Salary Range: $300,000 – $800,000 (commensurate with research impact and technical depth).
- Incentive Structure: Comprehensive package including sign-on bonuses, performance-based year-end equity/bonuses, and full relocation/immigration support.
- Operational Model: A high-collaboration hybrid framework (3 days in-office, 2 days remote) based in New York City.
When you partner with us, you are engaging with a team that speaks your language and understands the nuances of high-stakes innovation. We are committed to placing elite talent where their technical contributions drive systemic impact.
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