Machine Learning Engineer

TL;DR

Machine Learning Engineer: Architecting, implementing, and maintaining production-grade, low-latency ML services for ranking models, recommendation algorithms, and forecasting methods with an accent on model lifecycle and tuning hyperparameters. Focus on solving customer and business problems and enhancing AWS-native MLOps platform.

Location: We welcome applications from candidates based remotely within roughly two hours of the UK time zone, covering many European countries.

Company

hirify.global is the largest direct to consumer flower business in Europe, driven by their vision to create the destination for making life a little more thoughtful and beautiful.

What you will do

• Architect, implement, and maintain production-grade, low-latency ML services for ranking models, recommendation algorithms, and forecasting methods.
• Collaborate with data scientists, product managers and other teams to brainstorm best approaches for solving the problems at hand.
• Help design experimentations to test ideas and assess improvements to models.
• Deliver ML models with agreed engineering standards to ensure that capabilities are resilient, scalable and future-proof.
• Enhance AWS-native MLOps platform, and guarantee high availability and low-latency inference for models.
• Advise on data strategy to provide datasets for future data science projects.

Requirements

• Solid foundation in traditional ML techniques and the model lifecycle.
• Demonstrable experience designing, deploying, and monitoring ML services to solve customer and business problems.
• Strong programming skills in Python for delivering production-ready, well-structured and documented code.
• Experience with large datasets and proficiency with SQL.
• Experience with Snowflake and dbt is a plus.
• Positive, optimistic mindset.

Nice to have

• Experience working on an e-commerce site or in a fast-growing start-up.
• Experience working in a fully-remote setting.

Culture & Benefits

• Flexible working (core hours from 10-4pm).
• Work Abroad for up to 30 days each year.
• Mental health support.
• Flexible training framework for every stage of your career development.

Hiring process

• Chat with Senior Talent Acquisition Manager to run through experience, motivations and the role.
• Interview with Head of Data Science and Product Manager for Data Science team to get into more of the detail.
• Call with Lead Data Scientist and Head of Data Engineering to assess knowledge of data science and engineering principles.