Machine Learning Researcher

Machine Learning Researcher - Agentic Science Industry: AI / Biotechnology / Drug Discovery Location: USA - Fully Remote Salary: Competitive Base Salary + Equity As part of the continued development of their AI research platform, they're looking to hire a Machine Learning Researcher specialising in Agentic Science to develop intelligent systems capable of automating complex scientific research workflows. You'll work at the intersection of machine learning, AI agents, molecular modelling and scientific discovery, collaborating closely with computational scientists and domain experts to turn cutting-edge research into practical capabilities. This is a highly research-focused position where you'll have significant autonomy to define and lead projects, develop novel ML approaches, build rigorous benchmarks, and explore how agentic systems and foundation models can be applied to complex scientific problems. Key Requirements PhD in Machine Learning, Computer Science, or another STEM discipline involving the development of novel machine learning approaches. Strong track record of high-quality research, including publications, open-source contributions, or other recognised research outputs. Strong research or engineering experience across modern Machine Learning, Deep Learning or Statistical Modelling, with a strong understanding of ML theory. Demonstrated expertise in one or more relevant areas, including Agentic AI, In-Context Learning, Few-Shot Learning, Tabular Learning, Bioinformatics or scientific/clinical data modelling. Hands-on experience training, debugging and evaluating ML models using Python and frameworks such as PyTorch or JAX. Experience independently taking ambiguous research problems and translating them into well-defined ML projects, including datasets, task definitions, baselines, metrics and validation strategies. Strong experimental methodology, including designing benchmarks, ablations and evaluations that can distinguish genuine model improvements from bias or data artefacts. Excellent communication and collaboration skills, with the ability to work effectively alongside researchers and scientific domain experts. Training tabular foundation models, particularly in sparse, heterogeneous or small-data environments. Molecular Machine Learning or In-Context Learning applied to scientific or molecular datasets. Large-scale model training, including 1B+ parameter models, distributed training, sharding and large-scale data pipelines. Developing AI agent or \"co-scientist\" systems for scientific, physical or biological applications. Experience modelling biological, biochemical or clinical data using Machine Learning. #J-18808-Ljbffr