Staff Engineer, Machine Learning Life Sciences

About Inari

Inari is the SEEDesign™ company. We embrace the diversity and complexity of nature in every aspect of our business to drive innovation – to push the boundaries of what is possible. Through our unrivaled technology platform, Inari uses predictive design and advanced multiplex gene editing to develop step‑change products. We are taking a nature‑positive approach to unlock the full potential of seeds that will transform the food system.

The results will lead to more productive acres delivering value creation for farmers and a more sustainable future for our planet.

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. In this role, you will bring established best practices for building, deploying, and maintaining ML systems, and effectively apply that expertise in a life sciences context.

While life science experience is not a requirement, you are comfortable – or willing to become comfortable – working alongside biologists and reasoning about biological data. As a staff‑level individual contributor, you will drive major work streams with autonomy while collaborating closely with cross‑functional teams of computational biologists, software engineers, and crop scientists. This role is based in our Cambridge, MA office and follows our flexible hybrid work model, with time on a weekly basis split between in‑office and remote work.

• 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 work streams 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

• 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 (Num Py, Pandas, scikit‑learn) and hands‑on experience with PyTorch and/or Tensor Flow, including training and deploying neural networks
• Cloud & MLOps:
  Experience with AWS (EC2, S3, Sage Maker), 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

• 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,…