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New York, New York, United States
Onsite
Valthos Inc.
Valthos is an applied biological intelligence company. We build and deploy software and biological AI systems to safeguard humanity.
The same AI architectures that enable self-driving cars, land rockets with precision, and deliver expert-level reasoning are beginning to be deployed in biological design. To stay ahead, we must advance our arsenal of tools to capture and design against real-time sensitivities in nature’s evolving mutational landscape.
We are a group of mission-driven software engineers from Palantir and applied biological ML engineers from MIT’s Broad Institute and DeepMind making the latest advances in computational biology accessible in the real-world for federal and commercial use.
We are seeking a highly skilled, data-centric Applied AI Engineer - Core Capabilities to develop and apply state-of-the-art ML methods for assessing and responding to biological threats and for rapidly designing precision biologics.
Contribute to crafting and executing on the Valthos-wide research and development roadmap
Develop, build, and apply modeling approaches—including adapting and post-training biological frontier models—for tasks in biological security and the design of precision biologics
Develop, build, and apply evaluation frameworks to rigorously assess model performance on key tasks
Interrogate models to understand their biological learnings, capabilities, and limitations
Collaborate closely with computational biologists to understand the signal and artifacts inherent in biological data, and to create and curate datasets
Collaborate closely with software engineers to build, deploy, and scale model training and evaluation infrastructure
Visualize and communicate results within Valthos and externally
Work with customers and external collaborators to understand their needs and be an effective representative of Valthos
Embrace learning about areas—technical and non-technical—across Valthos
Stay up-to-date on the state-of-the-art methods at the intersection of AI and biology
Experience with data-centric development and evaluation of ML models
Highly proficient in Python and deep learning libraries (e.g., PyTorch)
Experience with cloud computing platforms (e.g., AWS) and distributed systems
Strong understanding of probability and statistics
Proven ability to design, implement, and evaluate new ideas in ML
Experience with pre- or post-training language models, developing reasoning agents, and/or time series modeling
Experience working with biological data or other types of noisy and heterogenous datasets
Experience with ML-centric bioinformatics and structure-based tools (e.g., AlphaFold)
Experience building data pipelining and training infrastructure
Contributions to open-source projects
If there is a fit, we'll schedule two technical interviews. The final step is an onsite in our office, where you'll work on a small project, discuss ideas, and work with the wider team.
