A-Alpha Bio · 1 week ago

Senior Scientist, Machine Learning

Seattle, WA

Full-time

Onsite

Senior Level

$166K/yr - $208K/yr

6+ years exp

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BiotechnologyHealth Care

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Responsibilities

Design and implement novel machine learning approaches for prediction and engineering of protein-protein interactions; use these approaches to solve challenges in drug discovery and optimization.

Generate deliverables (e.g., new code features, research reports, presentations, and data sets) to a high level of quality suitable for broad consumption within the company, as well as in accordance with assigned timelines.

Regularly and expertly contribute to the company codebase, authoring and reviewing merge requests and ensuring that new capabilities are effectively rolled out to the rest of the team.

Enthusiastically collaborate with the Machine Learning team, providing coaching, mentorship, and subject matter expertise to members of the broader Data Science department.

Perform other duties as assigned.

Qualification

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Deep LearningPyTorchTensorFlowPyGModel ArchitecturesCNNsVAEsTransformersGraph Neural NetworksGenerative AIDiffusionMachine Learning TheoryMathematicsData ScienceData ManipulationModel SelectionModel TrainingCross-ValidationDeployment at ScaleBiochemistryPhysical ChemistryMolecular PhysicsBiologyProblem-SolvingCommunicationPythonAmazon Web ServicesAzureGCPMachine Learning

Required

Bachelor’s degree in a related field with 6+ years of professional experience in machine learning, including 3+ years of direct experience with machine learning using biological sequence or protein structure data; or an equivalent combination of education and experience.

Extensive experience with deep learning, spanning use of modern software frameworks (PyTorch, TensorFlow, PyG); adapting, modifying, or originally developing model architectures (CNNs, VAEs, Transformers, graph neural networks); as well as familiarity with state-of-the-art generative AI approaches (diffusion, language models)

Deep understanding of underlying theory and mathematics for machine learning and ability to translate and implement it into code.

Strong expertise in data science and experience with manipulating/transforming data, model selection, model training, cross-validation, and deployment at scale.

Familiarity with scientific concepts in biochemistry, physical chemistry, molecular physics, and biology.

Excellent skills in communicating research results with both technical and non-technical audiences.

Preferred

PhD in a related field.

Domain expertise with Python and Amazon Web Services (or other cloud provider, such as Azure, GCP).

Familiarity with developing, deploying, and maintaining machine learning engineering systems for model training and inference, including aspects of CI/CD, MLOps, distributed computing, and/or data engineering.

Familiarity with the state of the art in antibody engineering, protein engineering, synthetic biology, or other high-throughput biology environments.

A record of scientific research publications and/or presentations in top-tier journals and conferences.