Senior Scientist​/Principal Scientist, Machine Learning

Senior Scientist/Principal Scientist, Machine Learning

Company: SLAS (Society for Laboratory Automation and Screening)

Terrana is seeking a senior Machine Learning Scientist to lead the development and integration of AI/ML methods that advance our understanding of RNA and peptide function in plant and microbial systems. The ideal candidate will have a strong background in modern AI/ML, professional software engineering experience, and familiarity with both computational biology and the molecular biology of plants and microbes. We are looking for someone who has applied structure-based machine learning, particularly generative AI, to solve complex modeling problems in scientific or industrial settings.

In this individual contributor role, you will report to the Chief Technology Officer and collaborate closely with senior leaders across Terrana and strategic partners. You will work with teams in data science, molecular biology, and plant science to integrate AI/ML with design-build-test-learn cycles to uncover how RNA and peptide structures mediate key biological functions. Beyond core scientific applications of AI/ML, you will help identify and implement opportunities to improve research and operational workflows using agentic systems and other emerging technologies.

• Design and implement ML/AI models to optimize in vivo RNA and peptide synthesis, dynamics, mobility, localization in plant systems.
• Predict how primary, secondary, tertiary structures of RNA and peptides relate to replication, mobility, physiological impact in plant cells.
• Develop and refine generative models to design novel RNA and peptide sequences optimized for uptake, trafficking and expression within plant systems.
• Design and curate scalable, queryable databases tailored for biological sequence data, structural annotations, experimental results to support generative learning and model retraining.

• Extend existing proprietary and public ML tools into discovery platforms that infer unknown RNA and peptide sequences from phylogenetic and functional data.
• Translate high-dimensional molecular data into testable biological hypotheses by uncovering principles of RNA and peptide behavior in plants and microbes.
• Increase experimental success rates by developing predictive models that prioritize high-potential constructs, accelerating Terrana’s test-and-learn cycles.

• Partner with teams across molecular biology, data engineering, and operations to embed AI/ML tools into scientific and operational workflows.
• Initiate and manage collaborations with internal and external partners to advance Terrana’s machine learning capabilities.

• Ph.D. or equivalent research experience in computer science, physics, biology, bioengineering, or a related field.
• Demonstrated ability to work with domain experts to translate biological constraints into machine learning objectives, especially for optimizing transport, stability, or expression in vivo.
• Familiarity with core problems and methods in computational biology.
• Familiarity with DNA/RNA synthesis constraints, codon optimization, and vector design.
• Experience with ontology design, data labeling, and metadata curation for heterogeneous biological datasets.
• Experience designing closed-loop ML systems or integrating active learning in high-throughput experimental contexts. Experience with synthetic biology platforms or high-throughput screening pipelines is a strong plus.
• Deep knowledge and expertise in machine learning, including generative foundation models, multimodal architectures, uncertainty estimation, and transformer-based methods.
• Hands‑on experience with deep learning frameworks and scalable computing environments (AWS preferred).
• Strong software engineering skills, with experience in Python or R and best practices for maintainable, reproducible code.
• Track record of improving workflows through automation, AI integration, or software tooling.
• Excellent communication and collaboration skills, with proven ability to translate between biologists and data scientists to define ML objectives rooted in real‑world biology.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status.