Member of the Technical Team - AI/ML Scientist
What you’ll accomplish with us:
Design and implement novel machine learning approaches—especially in pre-training, multimodal learning, and reinforcement learning—to make experimental science more reproducible, interpretable, and transferable at scale.
Design and develop AI models that can learn from experimental protocols, lab execution data, and failure modes to suggest, simulate, or guide future experiments.
Develop and implement data collection strategies—including computer vision, audio, and sensor-driven interfaces—to capture tacit knowledge embedded in physical lab workflows and human decision-making.
Become customer obsessed. Initiate, support, and lead program execution with external partners and collaborators to capture, document, and deliver results.
Develop and test internal and external benchmarks, validation strategies, and frameworks for testing reproducibility, robustness, and scientific utility.
Work alongside the biology and operations teams to identify automation opportunities, uncover latent structures in messy lab data, capture tacit knowledge, and improve experiment documentation and reproducibility.
Translate scientific hypotheses into computational experiments, analyzing model behavior and experimental results to draw actionable insights.
Stay on top of the latest research in ML/AI and evaluate its applicability to our platform. We will support opportunities to publish or present where appropriate to establish technical leadership.
Requirements:
Advanced degree in Computer Science, Machine Learning, or a related field.
Deep expertise in generative models, representation learning, multimodal learning, reinforcement learning, and/or causal inference.
Fluency in Python, modern ML libraries (e.g., PyTorch), and cloud infrastructure (e.g., AWS, GCP)
Demonstrated ability to design and implement ML models in noisy, complex, real-world settings—ideally involving biological or scientific data.
Additional preferences:
Experience in a scientific or research-intensive environment—academic labs, biotech R&D, national labs, or similar.
Familiarity with experimental workflows, lab automation, or scientific instrumentation.
Startup or early-stage experience preferred; comfort with ambiguity and rapid iteration is a must.
Ability to clearly communicate technical concepts to cross-functional teams and collaborate on projects spanning AI, biology, and product.
Ideal candidates are located within commuting distance from Cambridge, MA.