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. Our success is dependent on great minds, collaborating to generate bright ideas and deliver exceptional outcomes. We have 200 employees, with research sites in Cambridge, MA (USA) and Ghent (Belgium), as well as a product development site in West Lafayette, IN (USA). We’ve deliberately built a team that brings diversity of thought to all aspects of our business, to generate new ideas, approaches, and ways of operating. And we've intentionally combined experience with potential, bringing agriculture industry experts with the desire to innovate together with bright minds from academia, human therapeutics, software, and consulting. If you want to be part of a diverse and inclusive team developing unique solutions to feed the world while protecting our planet’s natural resources, we’d love to hear from you! 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. 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 workstreams 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. 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 You Bring… 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) Inari pays competitively and rewards results. ​ The salary range for this position is $148,530 - 204,250. Whether you are full-time or part-time, Inari provide three different components of pay – base, short-term incentive, and long-term equity along with a one-time new hire stock option grant. That's rare! ​ We also offer a comprehensive benefits package including both a Preferred Provider Network (PPO) and High Deductible Health Plan (HDHP) with a company-funded health savings account (HSA), vision, dental, and several flexible spending accounts (FSA), a host of voluntary benefits, and a robust wellness program. In addition, Inari offers a 401k plan with a company matching and a flexible paid time off policy. ​ Consistent with our commitment to pay transparency, salary range information is being disclosed. Please note that offers are based on the candidate's qualifications & experience as well as market demand. Inari is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Learn more about us: About Inari: https://inari.com/about-us/ Multiplex Gene Editing – The Key to Unlocking the Full Potential of Seed: https://inari.com/multiplex-gene-editing-matters-for-the-population-the-planet-and-the-people-who-grow-our-food/ The Promise of AI in Plant Breeding: https://inari.com/the-promise-of-ai-in-plant-breeding-2/ Unseen Upside Podcast: AI & Agriculture – Data-Driven Fields, Resilient Futures: https://www.cambridgeassociates.com/podcasts/agriculture/ AgTech Breakthrough AgTech Company of the Year: https://inari.com/inari-repeats-as-agtech-company-of-the-year/ Bloomberg: Inari AgTech Startup: https://www.bloomberg.com/news/articles/2025-01-07/agtech-startup-inari-raises-equity-at-2-17-billion-valuation BBC: Disaster-Proofing Crops: https://inari.com/bbc-news-how-crops-are-being-disaster-proofed/ Reuters: Gene Editing Wheat: https://www.reuters.com/markets/commodities/australian-trial-gene-edited-wheat-aims-10-bigger-yields-2024-05-23/#:~:text=CANBERRA%2C%20May%2023%20(Reuters),and%20make%20farming%20more%20sustainable Inari at Databricks Data + AI Summit: https://www.youtube.com/watch?v=pJG-r3ob-n0 Job Applicant Privacy Notice: https://inari.com/wp-content/uploads/2024/09/Job-Applicant-Privacy-Notice.pdf FOR U.S. CANDIDATES: Please note that we use the resume you submit with your application during our background check process. To ensure an efficient and accurate background verification, we kindly ask that you carefully review and accurately represent your work history, education and other relevant information on your resume. Any discrepancies or inaccuracies found during the background check may impact your candidacy for the position.