Staff Engineer, Machine Learning Life Sciences

About the role

Inari is seeking a Staff Machine Learning Engineer to join our AI Team in support of our mission of transforming agriculture through predictive design and advanced gene editing.

This role will focus on delivering production-ready ML pipelines using existing models while also exploring new modeling approaches to advance our ability to drive step-change trait improvement in crops.

This role will involve:

• Building, deploying, and maintaining production ML pipelines and infrastructure to serve predictions at scale, including model versioning, monitoring, and lifecycle management
• Integrating ML systems with genomic, phenotypic, and biological data platforms using AWS and containerization technologies
• Partnering with computational and experimental biologists to contextualize heterogeneous biological data and drive research-critical modeling programs
• Training and validating statistical and ML models; prototyping new approaches and evaluating feasibility for production deployment
• Implementing integrations with strategic third-party tools, foundation models, and AI agents; staying current with ML research to identify applicable methods
• Driving major workstreams autonomously while collaborating effectively with teammates and cross-functional stakeholders
• Communicating technical results clearly across disciplines and contributing to technical decisions, code reviews, and engineering standards

Responsibilities

As a Staff ML Engineer, Life Sciences you will…

• Build, deploy, and maintain production ML pipelines and infrastructure to serve predictions at scale, including model versioning, monitoring, and lifecycle management
• Integrate ML systems with genomic, phenotypic, and biological data platforms using AWS and containerization technologies
• Partner with computational and experimental biologists to contextualize heterogeneous biological data and drive research-critical modeling programs
• Train and validate statistical and ML models; prototype new approaches and evaluate feasibility for production deployment
• Implement integrations with strategic third-party tools, foundation models, and AI agents; stay current with ML research to identify applicable methods
• Drive major workstreams autonomously while collaborating effectively with teammates and cross-functional stakeholders
• Communicate technical results clearly across disciplines and contribute to technical decisions, code reviews, and engineering standards

Requirements

Required Education & experience:

• MS or PhD in Computer Science, Engineering, Statistics, Mathematics, Computational Biology, or related field (or BS with equivalent experience); 6+ years of ML engineering experience with a demonstrated emphasis on production systems
• Production ML: Proven ability to deploy, maintain, and monitor ML models and pipelines at scale
• Python & frameworks: Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks
• Cloud & MLOps: Experience with AWS (EC2, S3, SageMaker), containerization (Docker), experiment tracking (MLflow), and workflow orchestration (Airflow or equivalent)
• Cross-disciplinary collaboration: Comfortable interfacing with biologists and life scientists, translating between biological and ML framings, and communicating technical results to diverse audiences
• Ownership & drive: Track record of owning solutions and deliverables end-to-end — setting direction, aligning stakeholders, and seeing work through to impact — while remaining a collaborative and engaged team member

Strongly Preferred:

• Life sciences & bioinformatics: Familiarity with biological data types (genomic, transcriptomic, proteomic), common file formats (FASTA, GFF, VCF, BAM), and sequence modeling methods applied to DNA/RNA/protein data
• ML for biology: Awareness of current research in applying deep learning to biological sequences (e.g., genomic transformers, protein language models)
• Network analysis: Experience with graph neural networks or network analysis tools (e.g., networkx) for modeling complex biological relationships (e.g., gene regulatory networks, protein-protein interaction networks)

Qualifications

Required:

• MS or PhD in Computer Science, Engineering, Statistics, Mathematics, Computational Biology, or related field (or BS with equivalent experience); 6+ years of ML engineering experience with a demonstrated emphasis on production systems
• Production ML: Proven ability to deploy, maintain, and monitor ML models and pipelines at scale
• Python & frameworks: Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks
• Cloud & MLOps: Experience with AWS (EC2, S3, SageMaker), containerization (Docker), experiment tracking (MLflow), and workflow orchestration (Airflow or equivalent)
• Cross-disciplinary collaboration: Comfortable interfacing with biologists and life scientists, translating between biological and ML framings, and communicating technical results to diverse audiences
• Ownership & drive: Track record of owning solutions and deliverables end-to-end — setting direction, aligning stakeholders, and seeing work through to impact — while remaining a collaborative and engaged team member

Strongly Preferred:

• Life sciences & bioinformatics: Familiarity with biological data types (genomic, transcriptomic, proteomic), common file formats (FASTA, GFF, VCF, BAM), and sequence modeling methods applied to DNA/RNA/protein data
• ML for biology: Awareness of current research in applying deep learning to biological sequences (e.g., genomic transformers, protein language models)
• Network analysis: Experience with graph neural networks or network analysis tools (e.g., networkx) for modeling complex biological relationships (e.g., gene regulatory networks, protein-protein interaction networks)