Senior Applied Deep Learning Research Engineer

About OpeongoOpeongo is a seed-stage AI company building the Meshwork Virtual Machine — a compound foundation model of the extracellular matrix (ECM) remodeling network, and the ECM layer of the digital human. It learns shared representations of tissue state from multimodal experimental data, simulates remodeling dynamics, and predicts response to perturbation.Interested in this role You can find all the relevant information in the description below.We are hiring a Senior Applied Deep Learning Research Engineer to work on the model itself — thearchitecture, training, and evaluation of the Meshwork Virtual Machine.What you will work onFOUNDATION MODEL RESEARCH– Own research threads on the architecture of the Meshwork Virtual Machine — multimodalrepresentation learning, representation of heterogeneous biological measurements, simulation ofdynamics, and perturbation response prediction– Design and run training runs and ablations, and draw conclusions that survive scrutiny– Own evaluation that distinguishes real capability from leakage, batch artifacts, and wishful metrics– Bring methods from the wider machine learning literature into a domain that has not yet applied themENGINEERING AND INFRASTRUCTURE– Build the training and evaluation infrastructure the research requires– Develop data pipelines that turn heterogeneous experimental data into model-ready inputs– Own reproducibility for your work — experiment tracking, seeds, environments, and results otherscan rebuild– Make and defend practical decisions about compute, scale, and where efficiency actually mattersCOLLABORATION AND SCIENTIFIC CONTRIBUTION– Work directly with the biological team to turn biological questions into model questions, and resultsback into experiments– Learn enough of the underlying biology to ask good questions and to recognize when a result isbiologically implausible– Contribute to technical documentation, internal reports, and investor-facing scientific material– Share work externally where it serves the platform — publications, preprints, and open sourceProfileExperience or demonstrable ability is required in each of the following. Candidates are assessed on thesubstance of their work rather than on formal credentials alone.REQUIREMENTS– Command of modern deep learning — architectures, training dynamics, optimization, and evaluation– Hands-on experience training and evaluating large models on real data, beyond fine-tuning existingcheckpoints– Proficiency in Python and PyTorch or JAX, with the engineering ability to build experimentalinfrastructure independently– Judgment to own research direction rather than only execution — defining a problem, designing theexperiment that tests it, and determining when a result is sound– Ability to communicate clearly with scientists who are not machine learning specialists– Capacity to acquire domain knowledge in an unfamiliar field, and the rigor to do so properlyADVANTAGESDomain experience in the life sciences is an advantage rather than a requirement; the necessarybiological grounding is developed in role.– Experience with transformers, graph neural networks, diffusion or generative models applied tostructured scientific data– Foundation model work — pre-training, adaptation, or rigorous evaluation– Multimodal or cross-modal modeling, including work on modalities with mismatched scale and noise– Research experience at a frontier AI lab or a leading AI-for-science organization– A PhD in machine learning, computer science, or a related field– Exposure to biological, biomedical, or other high-noise experimental data– Open-source authorship or maintenance– Experience in a startup or early-stage research environmentHow to applySend a CV and a brief cover note to Links to publications, preprints, or code arepreferred over summaries of prior work. xhyhwjd Opeongo is an equal opportunity employer committed to fostering an inclusive workspace.