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machine learning engineer

Job Description

We believe small businesses are at the heart of our communities, and championing them is worth fighting for. We empower small business owners to manage their finances fearlessly, by offering the simplest, all-in-one financial management solution they can't live without.
About the role
We are looking for a Machine Learning Engineer who will strengthen our capacity to improve the scalability, maintainability and adaptability of our ML practice. This individual will be part of the ML team and report to the Director of Data.

The ML team is responsible for developing machine learning models as point solutions for our functional and product stakeholders within the business. We leverage MLOps to accelerate and systematize model development and the management of machine learning infrastructure.
Machine Learning is part of Wave’s wider Data function, and works closely with Data Engineers, Analytic Engineers, and Data Analysts who comprise the Analytics and Data Operations and Platform groups. Our collective strength as a Data Team comes from our relationships and close collaboration, enabling us to drive strategic and operational decision-making, and to advocate the data vision at Wave.

Here’s how you will make a difference

• Work closely within an Agile team of fellow ML Engineers and collaborate with Wave stakeholder teams to build and deploy models that address business objectives, solve complex problems, and simplify the lives of our small business customers.
• Apply your expertise to analyze and engineer features using vast amounts of data from multiple sources. Train and deploy models in production, monitor them for quality and adapt them as the data and business contexts evolve.
• Automate and maintain a system architecture that supports machine learning in processing more features, training and deploying more models, and observing batch and real time inference at scale.

This is what you need to succeed

• You have 3+ years of hands-on experience implementing and maintaining production machine learning systems.
• You possess a strong foundational knowledge in machine learning, and have trained and tuned a range of classification models using algorithms such as decision trees, gradient boosting, naive bayes, SVMs.
• You’re extremely comfortable with Python and SQL, and very familiar with AWS, Amazon SageMaker and Docker. Our stack includes SageMaker pipelines, Model Registry, AWS CodePipeline, Step Functions, CircleCI, S3, Redshift, Looker, as well as MLflow, DataDog, StreamLit for monitoring and performance checking.
• You have practical knowledge of MLOps and can build pipelines that train, tune and deploy models triggered by code changes, model degradation, and statistical drift.
• Practical knowledge and experience with natural language processing, large language models, vector databases and LLM frameworks like LangChain are a bonus.

You’ll thrive here if

• You’re self-motivated and have the ability to work autonomously. We count on you to get your work done, in ambiguous conditions, with tight deadlines, while still producing high-quality work. It’s fun, we promise!
• You are all about collaboration. You’ll be working within ML and Data, and with different teams across Wave. It’s not going to work if you don’t see the value of different perspectives.
• You are a stellar communicator. This means you know how to translate technical terms into non-technical language that is easy to understand.

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