ML Engineer - AI Coding Expert

Obsidian; Berlin; Vor Ort; EUR 394.000 - 561.000; Vollzeit

Zusammenfassung

Obsidian is seeking a skilled ML engineer to join a Frontier Code Agents project, evaluating and improving frontier AI coding models within realistic ML workflows. You will work on model evaluation, training pipelines, and deployment considerations, applying strong engineering judgment to practical problems.

Ideal candidates have 2+ years in ML engineering, hands-on experience with production systems, and familiarity with AI tooling such as Cursor, Claude Code, Codex, Windsurf or Gemini CLI.

Qualifikationen

• 2+ years of professional ML engineering experience.
• Experience building production ML systems and deployment infrastructure.
• Familiarity with LLM applications and AI-powered products.
• Experience deploying ML systems to production is preferred.

Aufgaben

• Use frontier AI coding agents to complete and evaluate complex ML and AI engineering tasks.
• Review model-generated implementations involving training, inference systems, MLOps, or LLM applications.
• Identify bugs, edge cases, performance issues, and failure modes.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Kenntnisse

ML engineering

Production ML

Model deployment

AI-powered products

ML tooling

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Jobbeschreibung

About the Role

• Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project.
• Contributors help evaluate and improve frontier AI coding models through structured technical assessments.
• The work focuses on realistic machine learning engineering workflows and model evaluation.
• Spots are limited and filling quickly on a first come, first serve basis.

What You'll Do

• Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and failure modes.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Time Commitment

• Sprint based project that runs in 12-24 hour stretches based on client requirement.

Compensation

• $400 per accepted task.
• Typical tasks take approximately 2-3 hours after ramp-up.
• Compensation is tied to accepted work.

Who Should Apply

• 2+ years of professional machine learning engineering experience.
• Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
• Experience deploying ML systems to production is preferred.