[Remote] Staff Research Scientist, Quantitative Systems Biology

Note: The job is a remote job and is open to candidates in USA. SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. We are seeking a highly skilled Research Scientist to anchor our next-generation computational biology models in deep Systems and Cellular Biology expertise, leveraging experimental data to enable cutting-edge computational modeling. Responsibilities • Formalize biological knowledge by translating literature and datasets into cell-type-aware causal and reaction-level frameworks for modeling. • Turn observations and model outputs into precise hypotheses and acceptance criteria that drive assay selection and experimental priorities. • Collaborate with modeling teams to design and validate perturbation-response models enforcing biological plausibility, uncertainty reporting, and traceability. • Guide interpretation of multi-omics data, accounting for experimental limitations and biases to ground models in real biology. • Drive experimental validation with partners by selecting assays and readouts, then close the loop by feeding results back into models and mechanism curation. • Apply model outputs to drug discovery problems, including target identification, mechanism of action inference, and prediction of cell-type-specific toxicity. • Build and maintain a living knowledge base of mechanisms, provenance, and assumptions to support reproducibility and regulatory-grade audits. • Work closely with the team to establish a novel benchmark suite for causal biological world models. Skills • PhD and Applied Work Experience in Quantitative Biology. PhD in molecular, cellular, quantitative, or systems biology with 1–3 years of postdoctoral or industry experience (biotech, pharma, or techbio) applying mechanistic biology to data-driven or computational research. • Proficiency in Python for Data Analysis. Demonstrated ability to write Python code for exploratory data analysis, and visualization. • Collaboration with Computational Biologists on Therapeutic Discovery Models. Experience partnering with data scientists and modelers to interpret, validate, and refine causal or predictive models for therapeutic discovery, target identification, or off-target assessment. • Construction and Curation of Causal or Reaction-Level Graphs for Modeling. Proven ability to extract, reconcile, and formalize biological mechanisms into structured causal or reaction-level representations that accurately capture regulation, modification, and molecular context for computational modeling. • Multi-Omics Data Integration and Understanding of Experimental Bias. Strong conceptual understanding of transcriptomic, single-cell, spatial, and proteomic data, including awareness of experimental limitations, data biases (batch effects, noise), and how these affect mechanistic inference and biological model accuracy. • Mechanistic Modeling for Therapeutics: Expertise in conceptualizing or applying quantitative models (e.g., GRNs, ODEs, Systems Pharmacology) to predict perturbation outcomes. Proven experience with causal inference/graph-based reasoning and applying structured benchmarks to test model validity and interpretability. • Integrative Multi-Omics and Data Synthesis: Hands-on experience integrating diverse multi-modal data (e.g., transcriptomic, proteomic, spatial, single-cell) to generate unified insights and contextualize model predictions. • Virtual Cell Model Engineering: Experience developing, interpreting, or rigorously evaluating virtual cell models to uncover mechanistic explanations and improve the fidelity of simulated drug response predictions. • Translational/Clinical Context: Familiarity with the data and modeling challenges specific to drug toxicity (ADME/Tox) and late-stage clinical data, addressing the high-cost-of-guesswork failure mode. • Cross-Functional Strategy and Alignment: Experience collaborating across scientific, engineering, and business teams to align modeling strategies, experimental design, and translational objectives. Benefits • Annual discretionary bonuses • Equity • 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 • More Company Overview • SandboxAQ develops AI and quantum technology solutions that enhance biopharma, cybersecurity, and materials science. It was founded in 2016, and is headquartered in Palo Alto, California, USA, with a workforce of 51-200 employees. Its website is https://www.sandboxaq.com. Company H1B Sponsorship • SandboxAQ has a track record of offering H1B sponsorships, with 6 in 2025, 6 in 2024, 3 in 2023, 5 in 2022, 1 in 2021, 5 in 2020. Please note that this does not guarantee sponsorship for this specific role.