Machine Learning Engineer, Causal Discovery

Summary

Join SandboxAQ's AI Simulation team as a Machine Learning Engineer to design and build causal machine learning systems for drug and materials discovery. You will leverage probabilistic graphical models, large-scale graph algorithms, and deep learning to analyze complex biological systems using multi-modal datasets. Collaborate with a high-performing team of scientists and engineers to advance scientific discovery. This role requires expertise in causal inference and experience with high-performance computing. The position offers a competitive salary, benefits, and opportunities for professional growth within a dynamic and innovative company. The work location is remote within the USA or Canada.

Requirements

• 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

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

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

Benefits

• Competitive salaries
• Stock options depending on employment type
• Generous learning opportunities
• Medical/dental/vision
• Family planning/fertility
• PTO (summer and winter breaks)
• Financial wellness resources
• 401(k) plans