[Hiring] Senior ML Specialist @Quartile

Role Description

The Senior ML Specialist is the team's authority on how machine learning models work and how to measure them. You will:

• Decide which models run which tasks and prove it with statistically sound benchmarks.
• Design the evaluation methodology the whole team relies on.
• Build classifiers and fine-tuned models where they beat prompting.
• Diagnose model behavior from first principles rather than by trial and error.
• Work inside a production codebase — shipping, operating, and monitoring what you design.

Qualifications

• 5+ years of applied machine learning experience, with a substantial part predating the current generative AI wave.
• Strong foundation in statistics and probability.
• Deep understanding of LLM and generative AI theory.
• Hands-on classical ML and NLP experience.
• Experience fine-tuning models and building datasets for it.
• Experience designing evaluation methodology for AI systems.
• Production-grade Python skills.
• Hands-on experience with at least one major LLM provider API.
• Master's or PhD in Machine Learning, Statistics, Computer Science, Mathematics, or a related quantitative field.
• The autonomy expected of a senior: you own a problem end to end.

Requirements

• Publications, research, or open-source contributions in NLP or machine learning.
• Experience with retrieval systems.
• Experience with agent evaluation.
• Experience with model cost and latency optimization.
• Experience with Databricks and Azure.
• Experience with observability stacks.
• Experience with the Model Context Protocol (MCP) or similar standards.
• Track record of shipping something from 0 to 1 in a fast-moving environment.

Benefits

• This is a PJ contract based in Brazil.

What You’ll Do

• Own model strategy across the platform.
• Design and maintain the evaluation methodology the team relies on.
• Build classification and fine-tuned models where they beat prompting.
• Design, develop, and ship agentic AI products on our platform.
• Do prompt and context engineering grounded in how models actually process context.
• Own output quality and measure whether the judges themselves are reliable.
• Diagnose model behavior from first principles.
• Build and extend agent tools that query Databricks, MongoDB, and external systems.
• Operate what you ship and monitor quality, latency, and cost.
• Collaborate with data engineers, software engineers, and product to deliver end-to-end AI solutions.
• Follow the research literature critically and bring in techniques that hold up on our data.
• Document and maintain the codebase, ensuring code quality and adherence to best practices.