Machine Learning Engineer, AI for Genomics

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 develop AI systems that are capable of reasoning across complex biological systems over multi-modal datasets—including genomics data, clinical information, and physics-based simulations.

In this role, you will work with a team to architect and train AI systems (eg. Foundation Models) that enable a deeper understanding of biological mechanisms and accelerate scientific discovery. You will bring expertise in Large Language Models, NGS sequencing pipelines, multi-modal data processing (especially multi-OMICS) 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 ML software for predictive and generative modeling tasks related to genomics data (eg. Interactome, Cell & Tissue modeling)
• Design and implement ML algorithms to enhance NGS sequencing pipelines 
• Apply reasoning techniques—including LLMs, Graph Neural Networks, Gen AI models—for extracting insights to advance drug discovery 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 and disease pathways using diverse multimodal data (e.g. multi-omics, single-cell data, proteomics, genomics, biomedical imaging)
• 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, Computational Biology, 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:
• Large Language Models and GenAI techniques
• NGS sequencing pipelines
• 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, Diffusion & Optimal Transport techniques)
• ML-based advancements in NGS sequencing pipelines 
• Biomedical Imaging
• Demonstrate good grasp 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
• Working knowledge of graph databases and graph data structures
• Strong publication record in peer-reviewed venues (eg. NeurIPS, ICLR, ICML, CVPR, ECCV, ICCV)
• Willingness to travel up to 25% for conferences, customer engagements, team offsites, or internal meetings

The US base salary range for this full-time position is expected to be $167k - $234k 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.