Staff Engineer, Machine Learning Life Sciences

Inari Agriculture, Inc.; North Oaks (MN); Hybrid; USD 148,530 - 204,250; Full time

Benefits offered by this job

Competitive salary

Stock option grant

Comprehensive benefits

401(k) with match

Hybrid work model

Job summary

Inari Agriculture, Inc. is seeking a Staff Machine Learning Engineer to join our AI Team. You will build and productionize ML pipelines, collaborate with computational biologists and software engineers, and drive major workstreams with autonomy in a cross‑functional setting.

You will apply Python, PyTorch/TensorFlow, and cloud tools to deploy models at scale, integrate with genomic data platforms, and stay current with ML research. Hybrid work model in Minnesota.

Qualifications

• MS or PhD in CS/Engineering/Statistics/Mathematics/Computational Biology (or BS with equivalent experience).
• Production ML: deploy, maintain, and monitor models and pipelines at scale.
• Python & frameworks: NumPy, Pandas, scikit-learn; PyTorch and/or TensorFlow.
• Cloud & MLOps: AWS (EC2, S3, SageMaker), Docker, MLflow, Airflow.
• Cross-disciplinary collaboration: interface with biologists and life scientists, translate biology to ML.
• Ownership & drive: own solutions end-to-end with stakeholder alignment.
• Familiarity with biological data types and sequence modeling; graph neural networks.

Responsibilities

• Build, deploy, and maintain production ML pipelines and infrastructure to serve predictions at scale.
• Integrate ML systems with genomic, phenotypic, and biological data platforms using AWS and containerization.
• Partner with biologists to contextualize heterogeneous data and drive modeling programs.
• Train and validate statistical and ML models; prototype new approaches for production feasibility.
• Implement integrations with third‑party tools, foundation models, and AI agents; stay current with ML research.
• Drive major workstreams autonomously while collaborating with teammates and stakeholders.
• Communicate results clearly and contribute to technical decisions and engineering standards.

Skills

Production ML

Python & frameworks

Cloud & MLOps

Cross-disciplinary collaboration

Ownership & drive

Biological data familiarity

Graph neural networks

Education

MS or PhD in CS/Engineering/Statistics/Math/Computational Biology

Tools

AWS

Docker

MLflow

Airflow

SageMaker

Job description

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. As an individual contributor at staff level, you will drive major workstreams with autonomy while collaborating closely with cross‑functional teams of computational biologists, software engineers, and crop scientists.

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.

Qualifications

• Required education and 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 an 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: 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 for modeling complex biological relationships.

Benefits

• Competitive salary range: $148,530 – 204,250.
• Compensation includes base, short‑term incentive, and long‑term equity with a one‑time new hire stock option grant.
• Comprehensive benefits package: PPO and HDHP with company‑funded HSA, vision, dental, flexible spending accounts, voluntary benefits, and a robust wellness program.
• 401(k) plan with company matching and flexible paid time off.
• Hybrid work model: weekly split between in‑office and remote work.

Inari is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.