ashby
ML Research Engineer, AI for Life Sciences
SandboxAQ
Remote, USA
Full-time
Mid
Remote
Discovered 1 weeks ago
Free
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About SandboxAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
The Opportunity
The AI Sim R&D team creates leading edge ML and physics-based models ("LQMs") to advance drug and materials discovery. We are a flexible, creative, and impact driven team of multidisciplinary scientists and engineers, whose products dramatically accelerate the creation of molecules and medicines.
As a ML Research Engineer, you will bring cutting edge research into production-grade reality, tasked with making in silico design the dominant paradigm in drug discovery. Your central purpose is to architect, scale, and optimize the scientific codebases that power our LQMs. Over your first year, you will drive the transition of high-impact prototypes into robust products, orchestrate distributed training pipelines on world-class GPU infrastructure, and pioneer hardware-level optimizations that push the boundaries of computational chemistry.
Key Responsibilities
Research, Architect, and Scale: Bring novel ideas and the content of scientific papers into high-performing and robust scientific code.
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