Member Technical Staff - AI​/ML Software Engineer

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. The transfer of discoveries from lab bench to real-world application has relied on informal handoffs, tacit know-how, and opaque processes. We’re building processes and cutting-edge AI tools 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. Our tools help scientists spend less time documenting and debugging and more time exploring. We believe this will become critical infrastructure to ensure discoveries reach their end customer: us.

Work with us:

Your impact starts here. We invite applicants across a broad range of expertise and experience. Whether you have bench experience addressing reproducibility challenges, are an early reader of AI benchmarking papers, or can quickly develop and deploy solutions that simplify tasks, you will be a member of the technical staff. Compensation will depend on experience and responsibility. Our team works across AI and biotech innovation, and we may consider you for other roles as well.

We think in-person collaboration is invaluable, but talent is broadly distributed, and we are open to flexible working arrangements. We’re based in Cambridge, MA, but invite applications from all geographies.

• 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 love bold and audacious science, enabling moonshots, and we work across the ecosystem to enable new and better futures
• We offer a full HR comp stack including competitive salary, equity, and benefits

• Develop end-to-end AI and software systems that enable reproducible and transferable science at scale
• Design and implement multimodal data pipelines (for both pre-training and reinforcement learning)
• Collaborate closely with scientists and engineers across teams to drive alignment and ensure knowledge flows efficiently
• Bring cross-functional initiatives from concept to delivery. Establish milestones, communication cadences, and decision-making frameworks
• Become customer obsessed. Initiate, support, and lead program execution with external partners and collaborators to capture, document, and deliver results
• Develop intuitive, usable tools and UIs in partnership with product and design to support scientist workflows
• Evaluate and integrate external tools, open-source models, and strategic partners
• Work alongside the biology and operations teams to identify automation opportunities and improve experiment documentation and reproducibility

• Strong programming expertise, with experience in software engineering, data systems, machine learning, and/or AI product and model development
• Fluency in Python, modern ML libraries (e.g., PyTorch), and cloud infrastructure (e.g., AWS, GCP)
• Demonstrated ability to turn ideas into functional prototypes. Ability to build quickly, test often, and refine thoughtfully—balancing prototypes with production quality as the company scales

• PhD or advanced degree in Machine Learning, Computer Science, or related field
• Experience in software engineering, data systems, machine learning, or AI product development in a scientific setting (academic or industry)
• Experience building AI/ML-powered products from ideation to deployment
• 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