About Teikametrics:

There has never been a more exciting time to join Teikametrics, the leading Retail Optimization Platform (ROP). We’re building an operating system to optimize every aspect of a retailer's business -- from advertising to inventory to pricing. We optimize billions of transactions for thousands of entrepreneurs and brands around the world selling on Amazon and other marketplaces. The Teikametrics ROP uses proprietary econometrics and machine-learning data models packaged in a simple SaaS interface. We combine our best in class technology with coaching and support from our world-class teams based in Boston, MA, Seattle, WA and Bengaluru, India. We are looking for people who can excel, add value to our mission, and thrive in a demanding start-up environment.

About the Role:

Teikametrics is looking for a Machine Learning Engineer to join a team under the Data Science directorate tasked with building a platform that supports the rapid construction and deployment of prediction and control services in the domain of E-commerce. Candidates should have an understanding of machine-learning and statistical modeling concepts paired with strong software engineering fundamentals. The Data Science team's predictive services are developed in Python on top of AWS Sagemaker, orchestrated by Airflow and backed by a data warehouse in Snowflake. Our back-end code emphasizes a ‘functional-first’ Scala stack with cats and fs2. The team's mandate is to design and build infrastructure that enables us to safely deploy solutions to the multi-agent, game-theoretic problems at the heart of decision-making in E-commerce. This entails model train/evaluate/serve lifecycle management, ML metadata storage and visibility, provisioning of separate environments for staging and integration testing, and task orchestration. The team will adopting existing solutions, like Kubeflow or AWS Sagemaker, where appropriate and fill in the rest.

Qualified candidates should have:

• 1+ years of experience working as a professional software developer.
• Proficiency in Python with exposure to some subset of the Python ML ecosystem (numpy/scipy/pandas, Tensorflow, etc.).
• Exposure to machine-learning model lifecycle; training, evaluation, serving.
• Interest in deploying machine-learning models as scalable services.
• Interest in "tool-making"; building features for developers that empower them and make them faster.
• Passion for learning and growing as a developer.
• Desire to work in a collaborative environment focusing on continuous learning; participating in tech talks, code review, and pair programming.

It's a bonus to have:

• Experience with any of: AWS, Airflow, Kubernetes, Terraform, Spark, Snowflake.
• Experience writing, testing, and interpreting numerical, scientific code.
• Experience with functional programming.
• Proficiency in SQL.