Machine Learning Engineer, Causal Discovery
Machine Learning Engineer, Causal Discovery
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$ 133 - 186
1 Vacancy
Job Description
About SandboxAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the worlds greatest challenges. The companys Large Quantitative Models (LQMs) power advances in life sciences financial services navigation cybersecurity and other sectors.
We are a global team that is tech-focused and includes experts in AI chemistry cybersecurity physics mathematics medicine engineering and other specialties. The company emerged from Alphabet Inc. as an independent growth capital-backed company in 2022 funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ weve cultivated an environment that encourages creativity collaboration and impact. By investing deeply in our people were building a thriving global workforce poised to tackle the worlds epic challenges. Join us to advance your career in pursuit of an inspiring mission in a community of like-minded people who value entrepreneurialism ownership and transformative impact.
About the Team
SandboxAQs 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 innovativeMachine Learning Engineer to drive causal inference capabilities across complex biological systems using multi-modal datasetsincluding 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 techniquesincluding Graph Neural Networks Probabilistic Graphical Models and LLMsfor 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 clientstailoring 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
- 35 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 dataincluding 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.
SandboxAQ welcomes all.
Employment Type
Full-Time
