Machine Learning Engineer, Causal Discovery

About the Team

SandboxAQ’s AI Simulation team is advancing the frontiers of drug and materials discovery by integrating physics-based simulations with cutting-edge AI. We are looking for an experienced and innovative Machine Learning Engineer to drive causal inference capabilities across complex biological systems using multi-modal datasets—including omics data, clinical information, and physics-based simulations.

In this role, you will design and build causal machine learning systems that enable a deeper understanding of biological mechanisms and accelerate scientific discovery. You will bring expertise in probabilistic graphical models, large-scale graph algorithms, and deep learning techniques for causal discovery, and collaborate closely within a high-performing, interdisciplinary team of drug discovery scientists, computational chemists, physicists, AI researchers, bioinformaticians, and software engineers.

Key Responsibilities

• Develop robust, scalable software systems that enable large-scale causal reasoning
• Design and implement algorithms to advance understanding of causality in complex biological systems
• Apply advanced graph-based reasoning techniques—including Graph Neural Networks, Probabilistic Graphical Models, and LLMs—for querying and inference over large-scale causal biomedical knowledge graphs constructed from simulation, omics data, and literature
• Identify, ingest, and curate relevant data sources. Own data quality control, validation, and integration workflows
• Research and prototype novel bioinformatics and deep learning approaches to interpret human genetic variants, gene regulation mechanisms, gene expression dynamics, and disease pathways using diverse multimodal data (e.g., clinical phenotypes, medical records, multi-omics, single-cell data, proteomics, genomics)
• Communicate complex ideas effectively across audiences, including internal collaborators, external stakeholders, and clients—tailoring technical depth as needed
• Contribute to the scientific community through patent filings, peer-reviewed publications, white papers, and conference presentations

Basic Qualifications

• Ph.D. in Computer Science, High-Performance Computing, or a related field
• 3–5 years of hands-on experience, preferably in the private sector, working on one or more of the following:
• Probabilistic or causal modeling
• Large-scale graph algorithms
• Graph neural networks
• Experience in processing and curating multi-modal data—including large-scale omics, clinical datasets, and scientific literature
• Proficiency in running analyses and training machine learning or deep learning models in high-performance computing (HPC) environments, particularly those using GPUs
• Strong collaboration mindset, with the ability to identify problems and communicate technical concepts clearly to both technical and non-technical stakeholders
• Demonstrated ability to dive deep into technically complex problems and a track record of driving initiatives through to completion

Preferred Qualifications

• Familiarity with advanced AI concepts, including:
• Generative AI (LLMs, Biological Foundation Models)
• Probabilistic Graphical Models (e.g., Bayesian Networks, Markov Networks, deep learning extensions)
• Causal inference (e.g., do-calculus, recent developments in causal discovery)
• Experience with cloud platforms such as Google Cloud Platform (GCP) or AWS for data storage and compute
• Working knowledge of graph databases and graph data structures
• Basic understanding of molecular biology concepts, particularly the central dogma (DNA, RNA, protein), and related high-throughput technologies such as RNA-seq, epigenomics, single-cell and spatial omics
• Strong publication record in peer-reviewed venues (eg. NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV)
• Willingness to travel up to 25% for conferences, customer engagements, team offsites, or internal meetings

Details

• Location: Remote (USA, Canada)

The US base salary range for this full-time position is expected to be $133k-$186k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.