Let's Do It!

About BeeHero:

BeeHero was founded in 2017 by a team of veteran beekeepers, serial
entrepreneurs, renowned biologists, and data scientists to ensure that
everyone wins: farmers, beekeepers, and nature.

‍

Our Mission:

To reimagine pollination and beekeeping in commercial agriculture by
leveraging data-driven technology to deliver predictability and precision to
the process.

‍

About The Role

We are seeking a talented Machine Learning engineer to join our team. In this role, you will be responsible for designing, building, and maintaining ML infrastructure that is efficient, scalable, and reliable. The ideal candidate should have a strong understanding of data science and machine learning concepts, proficiency in coding and software engineering, and knowledge of MLOps tools and technologies.

This is a hands-on technical leadership role. The Machine Learning engineer will be responsible for mentoring a data engineer in an expanding ML infrastructure team.

‍

Responsibilities

• Design, build, and maintain MLOps infrastructure that is efficient, scalable, and reliable.
• Set technical direction and priorities for the ML-infra.
• Monitor, troubleshoot and optimize the performance of machine learning models in production.
• Professionally mentor data engineers.
• Work with data scientists and software engineers to deploy and maintain machine learning models.
• Collaborate with other teams to ensure alignment and successful delivery of projects. Communicate technical solutions and trade-offs to non-technical stakeholders.
• Stay up-to-date with the latest trends and technologies in the ML field.
• Use data visualization tools to create visualizations that help data scientists and stakeholders understand the insights from the data.

‍

Qualifications

• 2+ years of experience in ML engineering and ML frameworks using MLOps tools: SageMaker, Airflow, Neptun
• 4+ years of hands-on experience in coding and software engineering with Python or other relevant languages.
• 3+ years of experience with cloud computing platforms.
• Knowledge of ML tools such as databases, data pipelines, and model deployment platforms.
• Strong mentorship and coaching skills, with ability to set technical direction while collaborating with cross-functional teams and stakeholders.
• A basic understanding of data science and machine learning concepts.
• Passion for continuous learning and staying up-to-date with the latest trends and technologies in the field.

‍

‍