Machine Learning Software Engineer

Mirai Bio, Inc is a Flagship backed, privately held biotechnology company on a mission to transform the current approach to information molecule therapeutics to unlock their full therapeutic potential. In recent years, we have begun to experience the power of information molecules in treating historically undruggable diseases and in designing therapies with unprecedented turnaround times. Mirai's platform integrates nanoparticle development with world-class informatics technologies and a novel pipeline of experimentation and discovery to drive a new generation of highly effective, therapeutically relevant information molecule therapies. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us

Mirai was founded by Flagship Pioneering. Flagship Pioneering conceives, creates, resources, and develops first-in-category life sciences companies to transform human health and sustainability. Since its launch in 2000, the firm has applied a unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $30 billion in aggregate value. The current Flagship ecosystem comprises 37 transformative companies, including: Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY), Indigo Agriculture, and Sana Biotechnology (NASDAQ: SANA).

Mirai Bio is seeking a highly talented individual to design, implement, and deploy novel ML approaches to optimize in vivo therapeutic delivery vehicles. They will work cross-functionally to build the computational tools necessary to identify and test therapeutic candidates to enable Mirai's next generation genomic medicines. The successful candidate will engineer scalable ML platforms and pipelines to overcome limitations of current nucleic acid delivery approaches and will have a strong understanding of targeted information molecule delivery. A successful candidate will have strong familiarity with production-grade ML systems, active learning, and experience working with chemoinformatic libraries. The candidate will also be expected to collaborate extensively with experimental and infrastructure teams.

• Design, build, and deploy production-grade ML models for optimization of lipid nanoparticle (LNP) formulation, synthesis, and in vivo performance.
• Develop and implement scalable software best practices for uncertainty quantification, ensuring models are reliable for real-time decision-making.
• Engineer robust data pipelines to integrate multi-fidelity datasets (in silico and in vivo) to accelerate data-driven discovery of novel LNPs
• Collaborate with computational scientists to translate research models into maintainable, efficient software that identifies design pathways for LNPs that achieve targeted functional properties
• Work with infrastructure and automation teams to architect and streamline real-time data