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SLAS (Society for Laboratory Automation and Screening) · 4 days ago

Senior Scientist/Principal Scientist, Machine Learning

Cambridge, MA

Full-time

Onsite

Senior Level, Lead/Staff

Terrana Biosciences is a cutting-edge agricultural biotechnology company developing innovative RNA-based products. They are seeking a senior Machine Learning Scientist to lead the development and integration of AI/ML methods to advance understanding of RNA and peptide function in plant and microbial systems.

AssociationBiotechnologyCommunitiesGovernmentInformation Technology

Growth Opportunities

Responsibilities

RNA and Peptide Function Prediction: Design and implement ML/AI models to optimize in vivo RNA and peptide synthesis, dynamics, mobility, and localization in plant systems

Predict how primary, secondary, and tertiary structures of RNA and peptides relate to replication, mobility, and 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, and 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

Qualification

Machine LearningGenerative AIComputational BiologyDeep Learning FrameworksPythonRSoftware EngineeringData LabelingOntology DesignHigh-Throughput ScreeningCommunicationCollaboration Skills

Required

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

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