Staff Engineer, Machine Learning Life Sciences

Inari•Cambridge, MA

35d•$148,530 - $204,250•Hybrid

About The Position

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.

Requirements

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
Proven ability to deploy, maintain, and monitor ML models and pipelines at scale
Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks
Experience with AWS (EC2, S3, SageMaker), containerization (Docker), experiment tracking (MLflow), and workflow orchestration (Airflow or equivalent)
Comfortable interfacing with biologists and life scientists, translating between biological and ML framings, and communicating technical results to diverse audiences
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

Nice To Haves

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
Awareness of current research in applying deep learning to biological sequences (e.g., genomic transformers, protein language models)
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)

Responsibilities

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

Benefits

Base pay
Short-term incentive
Long-term equity
One-time new hire stock option grant
Preferred Provider Network (PPO) health plan
High Deductible Health Plan (HDHP) with a company-funded health savings account (HSA)
Vision insurance
Dental insurance
Several flexible spending accounts (FSA)
Voluntary benefits
Wellness program
401k plan with company matching
Flexible paid time off policy