Applied AI Engineer — Remote, Equity, Prompt & Evaluation

tend; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity 0.8%–1.2%, vesting over four?ny

Remote work across US/Europe

Job summary

tend is seeking an engineer to improve AI powered product experiences by making third party model APIs more accurate, responsive, and useful to customers. The role focuses on applied engineering, developing prompts, evaluation systems, and product integrations, with emphasis on voice interactions.

You will build evaluation pipelines, assess new models with rigorous tests, and integrate LLM APIs into software to balance quality, cost, and latency for real customer workflows.

Qualifications

• At least three years of software engineering experience.
• Hands on experience with LLM APIs such as OpenAI or Anthropic.
• Strong prompt engineering skills and a methodical approach to improving outputs.
• Experience building evaluation pipelines for AI systems.
• Proficiency in TypeScript or Python.
• A strong focus on measuring quality and making it better over time.

Responsibilities

• Improve the quality of model outputs through systematic prompt development and iteration.
• Build evaluation pipelines that measure AI behavior and help track quality across changes.
• Assess new models with evaluations and use measured results to inform product choices.
• Integrate LLM APIs into software experiences, balancing response quality, token usage, and latency.
• Apply AI capabilities to practical customer workflows where usefulness matters more than experimentation for its own sake.

Skills

Software engineering

Prompt engineering

Evaluation pipelines

LLM APIs

Quality measurement

Tools

OpenAI API

Anthropic API

TypeScript

Python

Job description

tend is seeking an engineer to improve AI powered product experiences by making third party model APIs more accurate, responsive, and useful to customers. The role focuses on applied engineering, developing prompts, evaluation systems, and product integrations, with emphasis on voice interactions.

You will build evaluation pipelines, assess new models with rigorous tests, and integrate LLM APIs into software to balance quality, cost, and latency for real customer workflows.