We are looking for a passionate and experienced Senior-level Machine Learning/Data Engineer who has been doing real-world ML-focused development for 8+ years with backend development experience.

You have formal training in Computer Science, ML and/or Software Engineering.

You have experience with designing, building and maintaining large-scale distributed machine learning (including recommendation systems, and NLP) technologies coupled with a solid understanding of database and data analytics.

You have experience in most of the following (we have indicated our expectations of your skill level in each as one of the following: F- Familiar / I – Intermediate / E- Expert):

Python / sci-kit-learn / Pandas (E)

SQL (E)

BigQuery, Google Data Studio, GCP (F)

Tensor flow and/or PyTorch (F)

SW End techniques (CI/test driven development/etc) (I)

Technical areas and tools that are “Nice to have”:

PHP / Laravel

ChatGPT or other Generative AI / Large Language Model technologies

dbt

You have strong written and verbal communication skills. We are a very team-oriented company working remotely with colleagues across the world, so clear communication is central to our success.

You have the ability to balance speed and features – based on the task at hand, you strike a balance between working efficiently and adding new features.

You have a data-driven, experimental approach to making decisions.

You are proactive, curious and communicate continuously. Given a problem, you collaborate with others and keep everyone in the loop along the way.

You are open to thinking differently – Some designs need to be constrained within technical and/or architectural boundaries and other times designs need outside-the-box thinking! As a senior IC, you are able to understand what a given situation calls for and support designs on both ends of this spectrum.

Key Responsibilities

The Senior ML/ Data Engineer is a hands-on role, you will:

Collaborate with team members to understand requirements and opportunities for ML systems

Design, implement, test, maintain, monitor and improve scalable ML models

Design, build, and maintain scalable and efficient data pipelines and data infrastructure to support data processing, analytics and ML requirements.

Develop data models, ETL workflows, and data quality checks to ensure data accuracy and consistency.

Ensure data security and privacy are maintained across all data processing and storage systems.