Transfyr Bio
Member of the Technical Staff - AI/ML Scientist
Transfyr Bio, Cambridge, Massachusetts, us, 02140
Overview
We’re leveraging AI to solve one of the most consequential challenges in the pursuit of technical progress: the distribution of scientific innovations to the real world. We’re well-funded with top investors and are building a world class team. For innovations to have broad impact, they must scale beyond the lab. We’re building processes and cutting-edge AI tools that aim to illuminate and streamline these critical transfers of knowledge, transforming experimental science into robust, scalable solutions. We aren’t building AI to replace human ingenuity, but rather AI that can be a better partner in understanding and translating that ingenuity to new domains. We seek to build tools that help scientists spend less time documenting and debugging and more time doing the exploration they love. We believe this will become critical infrastructure to ensure discoveries reach their end customers.
Work with us:
Your impact starts here. We enthusiastically invite applicants across a broad range of expertise and experience to apply. Maybe you have expertise at the bench and have seen reproducibility challenges first hand, maybe you’re one of the first people to read a new AI benchmarking paper, or you have a knack for quickly developing and deploying solutions that make mundane tasks easier. Each candidate that joins the team will be a member of the technical staff, and compensation will depend on experience and responsibility. Our team works across AI and biotech innovation, so even if you apply for one role, we may consider you for others as well. We think in-person collaboration is invaluable, but recognize that talent is broadly distributed, and are open to flexible working arrangements. We’re based in Cambridge, MA, but invite applications from all geographies. What you can expect from us
Opportunity to join a creative and mission-oriented founding team and build
with
us, from the ground up We have a bias for action and are obsessed with solving our customers’ real problems We also love bold and audacious science, enabling moonshots, and are motivated to work with and across the entire ecosystem, enabling new and better futures for ourselves and our families We also offer a full HR comp stack to include competitive salary, equity, and benefits 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.
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We’re leveraging AI to solve one of the most consequential challenges in the pursuit of technical progress: the distribution of scientific innovations to the real world. We’re well-funded with top investors and are building a world class team. For innovations to have broad impact, they must scale beyond the lab. We’re building processes and cutting-edge AI tools that aim to illuminate and streamline these critical transfers of knowledge, transforming experimental science into robust, scalable solutions. We aren’t building AI to replace human ingenuity, but rather AI that can be a better partner in understanding and translating that ingenuity to new domains. We seek to build tools that help scientists spend less time documenting and debugging and more time doing the exploration they love. We believe this will become critical infrastructure to ensure discoveries reach their end customers.
Work with us:
Your impact starts here. We enthusiastically invite applicants across a broad range of expertise and experience to apply. Maybe you have expertise at the bench and have seen reproducibility challenges first hand, maybe you’re one of the first people to read a new AI benchmarking paper, or you have a knack for quickly developing and deploying solutions that make mundane tasks easier. Each candidate that joins the team will be a member of the technical staff, and compensation will depend on experience and responsibility. Our team works across AI and biotech innovation, so even if you apply for one role, we may consider you for others as well. We think in-person collaboration is invaluable, but recognize that talent is broadly distributed, and are open to flexible working arrangements. We’re based in Cambridge, MA, but invite applications from all geographies. What you can expect from us
Opportunity to join a creative and mission-oriented founding team and build
with
us, from the ground up We have a bias for action and are obsessed with solving our customers’ real problems We also love bold and audacious science, enabling moonshots, and are motivated to work with and across the entire ecosystem, enabling new and better futures for ourselves and our families We also offer a full HR comp stack to include competitive salary, equity, and benefits 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.
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