🤖 Applied AI Engineering – Byteboard

Domain Overview

Applied AI engineers apply artificial intelligence and machine learning techniques to develop applications and systems across various industries. Unlike ML engineers, who focus more on developing and deploying machine learning models, an applied AI engineer works closely with cross-functional stakeholders to develop products that integrate machine learning models. An Applied AI engineer has a strong foundation in both software engineering and AI concepts, which can help companies looking to incorporate AI into their product offerings.

The Applied AI Engineering Assessment is intended to be used as one of the first interview rounds to effectively capture signal on the general skillset for this role, which involves both software engineering and AI expertise.

Variants Offered

Don’t see a specialized skill your team would like for this role? Variants offer a way to add tasks that measure additional skills outside of the general role profile. Reach out to cs@byteboard.dev to learn about how to request specialized variants for your role.

Part 1 - Technical Design Review (40 minutes)

A document writing exercise where candidates can edit and contribute to technical documentation. They can collaborate with teammates and demonstrate their ability to communicate via document comments.

Skills & Subskills Measured

Analyzing ML Models

Communication

Tradeoff Analysis

User-Centered Design

Task Focus Areas

Focus Areas	Context	Evaluation Criteria
Designing AI-Forward Systems	Candidates will be asked to collaborate on a technical design document for a new feature and provide recommendations on how to implement the feature in an AI-forward manner.	Candidates are expected to address major fallbacks of the proposed feature, provide well-reasoned alternatives, and recommend best practices for improving machine learning models.
Navigating Business Requirements	Candidates will collaborate with stakeholders and propose technical solutions while considering the stated business goals.	Candidates are expected to promote reasonable technical solutions while balancing organizational constraints and will be measured by their ability to clearly explain their reasoning.

Part 2 - Coding Exercise (70 minutes)

A code implementation exercise where candidates can contribute to a complex multi-file code base while making increasingly difficult design choices.

Skills & Subskills Measured

Code Structure

Edge Case Thinking

Logical Reasoning

Productivity

Translating Ideas To Code

Writing Efficient Code

Task Focus Areas

Focus Areas	Context	Evaluation Criteria
Writing Code	Candidates will work on implementing coding tasks related to the feature described in Part 1.	Candidates are expected to write clean, correct code that addresses all task requirements.
Designing Code Architecture	Candidates will work on increasingly open-ended coding tasks, requiring them to make larger design choices across the codebase.	Candidates are expected to make architectural changes to the codebase while considering new edge cases and making reasonable assumptions about code behavior.