Question 1

What kind of forecasting models does Braintrust use for ticket predictions?

Accepted Answer

Braintrust utilizes various models, starting with baseline approaches, and iterating towards more accurate and performant solutions. Candidates should be comfortable with applied ML and time series forecasting techniques, ready to propose and implement suitable models for ticket price and count predictions.

Question 2

How does Braintrust ensure data quality for ML in production?

Accepted Answer

Data hygiene and validation are crucial at Braintrust. As a Senior Backend Engineer Forecasting & ML in Production, you'll be expected to implement pragmatic approaches to manage large datasets, ensuring data quality for accurate model training and reliable inference in production.

Question 3

What is the team structure like for the Senior Backend Engineer Forecasting & ML in Production role at Braintrust?

Accepted Answer

You'll be working collaboratively within a team environment, partnering closely with existing backend engineers. The role involves defining initial approaches, integrating forecasts into microservices, and supporting ongoing iterations, suggesting a collaborative and integrated team dynamic.

Question 4

What is the expected latency performance for ML inference at Braintrust?

Accepted Answer

A key responsibility for this role at Braintrust is to deploy inference in production and optimize for latency and performance. While specific targets aren't listed, the expectation is to deliver efficient and fast predictions for ticket prices and counts, which are critical for the SaaS analytics platform.

Question 5

What level of Node.js experience is beneficial for this Braintrust role?

Accepted Answer

While strong Python is a must-have, Node.js experience, particularly in a microservices environment, is a valuable nice-to-have for the Senior Backend Engineer Forecasting & ML in Production role at Braintrust. It will aid in seamless integration of forecasting features into existing services.

Question 6

What is Braintrust's approach to model monitoring and data drift?

Accepted Answer

Braintrust emphasizes adding lightweight monitoring for model performance and data drift indicators. This proactive approach ensures the reliability and accuracy of forecasting features post-deployment, allowing for timely adjustments and continuous improvement.

Senior Backend Engineer Forecasting & ML in Production

Braintrust

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