Staff Engineer, Machine Learning Life Sciences

Inari

$148K — $204K *
Pharmaceuticals & Biotech
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • MS or PhD in relevant field or equivalent experience.
  • 6+ years of ML engineering with a focus on production systems.
  • Advanced proficiency in scientific Python and frameworks like PyTorch or TensorFlow.
  • Experience with AWS, containerization, and MLOps tools.
  • Ability to collaborate with biologists and translate technical concepts.
  • Proven track record of delivering end-to-end solutions.

Responsibilities

  • Build, deploy, and maintain ML pipelines and infrastructure.
  • Integrate ML systems with biological data platforms.
  • Partner with biologists to drive modeling programs.
  • Train and validate ML models, prototyping new approaches.
  • Implement integrations with third-party tools and stay updated on ML research.
  • Drive major workstreams with autonomy while collaborating with teams.
  • Communicate technical results effectively across disciplines.

Benefits

  • Competitive compensation with multiple components (base, incentives, equity).
  • Comprehensive health plans (PPO and HDHP) including HSA.
  • 401k plan with company matching.
  • Flexible paid time off policy.
  • Robust wellness program and various voluntary benefits.
Full 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.

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.

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