Machine Learning Researcher – Agentic Science

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🔥 1 minute ago

🇺🇸 United States – Remote

💵 $200k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

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PostEra

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

🏥 Healthcare

🤖 Artificial Intelligence

🧬 Biotechnology

Healthcare • Artificial Intelligence • Biotechnology

PostEra is a modern, 21st-century biopharma company that leverages its AI platform, Proton, to enhance and accelerate the discovery of new medicines. The company focuses on medicinal chemistry powered by machine learning, aiming to tackle the challenges associated with drug discovery. PostEra has established collaborations with major pharmaceutical entities like Pfizer and Amgen, emphasizing its role in the advancement of AI-driven drug development.

📋 Description

• Develop the agentic research vertical at PostEra using agentic systems to automate development of mechanistic models for biochemical and physiological processes • Analyse biological data for target validation and use models to drive drug discovery decisions • Develop machine learning methods that adapt to new drug discovery problems from limited labeled data • Build molecular and tabular in-context learning systems and foundation models from proprietary multimodal data • Determine relevant prior examples and tasks, quantify when transfer is helpful or harmful, and provide reliable predictions under distribution shift • Define tasks, construct datasets and evaluation episodes, develop baselines, train and scale models, perform rigorous ablations, and translate successful methods into capabilities used by scientists • Develop and benchmark agentic systems for quantitative biological and physiological models • Independently identify, formulate, and lead research projects involving in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning • Design and train models adapting to new assays, endpoints, targets, or chemical series • Rigorously compare approaches against strong baselines and curate bias-sensitive test cases • Collaborate with scientists on potency modeling, ADME prediction, selectivity, lead optimization, and early clinical study design • Develop efficient training and data pipelines and scale models across molecular and tabular tasks • Produce readable, reproducible research code; track experiments; contribute to code review, documentation, and shared modeling infrastructure • Publish results in leading machine learning, medicinal chemistry, or computational biology venues and represent PostEra to the scientific community

🎯 Requirements

• PhD degree in machine learning, or STEM research involving the development of novel machine learning approaches • Track record of high-quality research, such as publications and open-source contributions • Strong research or engineering experience in modern machine learning, deep learning, or statistical modeling, backed by understanding of the theory behind machine learning algorithms • Demonstrated expertise in at least one relevant area: agentic workflows for science, machine learning approaches to bioinformatics and clinical data modelling, in-context learning, tabular learning, few-shot learning • Hands-on experience training, debugging, and evaluating ML models in Python using frameworks such as PyTorch or JAX • Ability to independently translate ambiguous scientific or technical problems into well-defined ML projects, including datasets, task definitions, baselines, metrics, and validation schemes • Ability to design careful experiments, benchmarks, and ablations that distinguish improvements from biases, and understand which aspects of the model led to the improvements • Comfort working in a startup environment where priorities evolve, data is imperfect, and high-quality judgment matters as much as raw model complexity • Prior drug discovery experience is not required • Nice-to-have: experience training tabular foundation models, particularly for sparse, heterogeneous, small-data, or high-missingness settings • Nice-to-have: developing molecular in-context learning systems or adapting general-purpose in-context models to molecular or scientific data • Nice-to-have: large model training, including 1B+ parameter models, distributed training, sharding, data parallelism, model parallelism, and large-scale data pipelines • Nice-to-have: development of AI “co-scientist” systems for physical or biological problems • Nice-to-have: hands-on experience modelling biological, biochemical or clinical data using machine learning approaches • Nice-to-have: moving research models into production scientific software or computational workflows

🏖️ Benefits

• Equity: 0.05 - 0.1% • Proportional compensation • Internal recognition • Meaningful promotions