• Building experiments, debugging models, scaling training pipelines, and turning research ideas into working systems. • Work closely with scientists and other engineers to implement new methods, run large-scale experiments, and help shape the infrastructure that supports research programs.

• Define the 'Reddit Benchmark' evaluation standard: Own the methodology — not just the harness — for rigorously measuring model quality across Safety, Reasoning, representation/retrieval, and Reddit-specific knowledge. Decide what 'Reddit-native' means in measurable terms and set the bar the org trains against. • Own evaluation reliability and statistical rigor: Establish the science behind trustworthy evals — judge variance, multi-sample scoring, inter-rater/inter-sample agreement, sampling and temperature effects, and calibration of automated judges. You are accountable for whether a benchmark delta is real or noise. Drive the practice of evaluation as a release gate — offline against frozen datasets, and pre-merge in CI/CD — so regressions are caught before endpoints ship. • Design model-as-a-judge methodology: Own judge selection, prompt design, calibration, and reliability for automated evaluation using frontier external models, enabling rapid, trustworthy iteration cycles. • Set post-training recipes and strategy: Design SFT recipes (data mixtures, curriculum, ablation strategy) that convert base models into helpful, well-aligned endpoints; partner with engineering to scale them. • Evaluate base and CPT checkpoints, not just endpoints: Design checkpoint-selection methodology across CPT experiments and LR studies, so we pick the right base before committing post-training compute. • Drive synthetic data generation strategy: Define and curate high-quality instruction and evaluation sets to improve generalization where human data is scarce. • Partner with Safety Engineering: Translate high-level safety policy into concrete classification metrics, probe sets, and CI/CD unit tests — including precision/recall at threshold, label-noise handling, and false-positive taxonomy for abuse detection (HHV). • Diagnose post-training instability: Dive into loss curves and eval logs to identify alignment tax and capability degradation, and recommend the fix. • Lead research direction: Set technical direction for evaluation and post-training across the team, mentor engineers and scientists, and represent the work internally (and externally where appropriate).

• Design, train, and ship production-grade ML models, including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features. • Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities. • Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance. • Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability. • Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints (e.g., overfitting vs. underfitting, compute efficiency). • Collaborate cross-functionally with data engineers, product managers, and software engineers to translate business requirements into technical ML specifications and integrate models into user-facing applications. • Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health. • Stay at the forefront of ML research, evaluating novel algorithms and techniques (e.g., Transformer architectures, quantization) to drive innovation and technical strategy.

Headquartered in Silicon Valley, we are a newly established start-up where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of generative AI. Our team comprises leading minds and innovators in AI and biological science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine. We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our robust R&D team and leadership in LLMs and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris and Abu Dhabi, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI. Job Description: You will work with the team to conduct cutting-edge research in AI, foundation models, and computational biology. Your primary tasks will include improving existing models and exploring new methodologies to advance our AI capabilities in biology. You will collaborate with the team on designing and executing large-scale experiments, analyzing complex datasets, and applying statistical techniques to validate the performance and robustness of AI systems. Additionally, you will work closely with AI/machine learning researchers and computational biologists to develop Genbio AI’s state-of-the-art biology foundation models and drive the research agenda to generate impact. Qualification: