Staff ML Ops / Platform Engineer

Key Skills

Amazon EMR, Analytical and Problem solving, Apache Airflow, AWS, AWS Sagemaker, AzureML, Continuous Integration & Continuous Delivery - CI/CD, Databricks, Docker, Machine learning techniques, MLflow, Nextflow, Python Programming, Terraform, VertexAI

Job Description

ABOUT THE ROLE...

Do you want to empower Data Scientists, Bioinformaticians, Quantitative geneticists and crop scientists and help transform the global food system to support the population, the planet and the people who grow our food? If so, the Inari AI team is looking for a Senior ML Ops / Platform Engineer to help grow our Data and Machine Learning platform, enabling Inari to drive major advances at the intersection of breeding, gene editing, and agronomy. 

As a member of our machine learning engineering team, you will be responsible for developing and maintaining the platform that supports predictive design across all of Inari: from exploration to production inferences, supporting our data scientists across science and product teams.

LOCATION: Cambridge, MA; West Lafayette, IN or Ghent, Belgium

WHAT YOU’LL DO

• Actively design, plan, and implement changes and advancements to our ML automation and infrastructure.
• Work cross-functionally with data scientists and bioinformaticians to understand the various use cases and support pipelines and processes that are fundamentally changing how we grow food. Build, test, and document self-service tools enabling them to accelerate their work with advanced ML infrastructure.
• Set standards on how to run a variety of workflows for machine learning and accelerated computing on the cloud and provide input on best practices for data, e.g., genomics and phenotypic data, intensive workloads.
• Implement continuous integration and continuous deployment (CI/CD) for machine learning models and data pipelines.
• Lead and orchestrate the enforcement of data governance and management strategy across Inari’s Cloud Data Platform.
• Create and operate pipelines for batch computation and inference of productions models.

• Develop robust integrations with strategic third-party tools, platforms and models.

YOU BRING:

• 4+ years of ML or ML Ops experience in a production environment
• 4+ years of experience with cloud computing platforms (AWS preferred) in general and ML platforms in particular (SageMaker, VertexAI, AzureML). Ideally, including infrastructure as code practices with tools like Terraform.
• 2+ years of experience with container-based deployments (e.g. Docker, Kubernetes).
• 2+ years of experience in at least one ML lifecycle management (e.g. MLflow) and workflow automation (e.g. Airflow, Nextflow).
• Proficient in Python and familiar with its data-centric libraries.
• Experience working and supporting data platforms such as Databricks, EMR, or similar platforms.
• Experience with CI/CD practices.
• Experience building auto-scaling ML systems.
• Experience building and maintaining cross-functional integration tools for Computational scientists.
• Strong problem-solving skills and ability to troubleshoot complex system issues.
• Excellent communication skills, with the ability to explain complex concepts to non-technical team members.
• A strong commitment to our team’s values: learn from every decision, no heroes/heroines, thoughtful inclusion, and fail fast.
• Active interest in learning new technology or domains. We are an organization that goes across many disciplines.
• Desire to work in a mission driven organization focused on sustainability and how we grow food. 

BONUS POINTS:

• Experience working with agtech or biotech.
• Experience working with computational scientists and understanding their diverse needs.