Machine Learning Engineer - Fully Remote | Upto $85/hr

Obsidian; Toronto; Remote; CAD 183,000 - 276,000; Full time

Job summary

Obsidian is seeking a contractor to join a frontier AI coding project. You will use frontier AI coding agents to perform and evaluate complex ML engineering tasks, focusing on realistic workflows, model training, and deployment considerations.

Candidates should have 2+ years in ML engineering, experience deploying ML systems, and familiarity with tools like Cursor, Claude Code, Codex, Windsurf, or Gemini CLI. This is a sprint-based, remote opportunity with compensation per accepted task.

Qualifications

• 2+ years of professional ML engineering experience.
• Experience building production ML systems and deployment infrastructure.
• Familiar with LLM applications and AI-powered products.
• Ability to evaluate model-generated ML implementations and technical tradeoffs.
• Experience deploying ML systems to production is preferred.

Responsibilities

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

Skills

ML Engineering

Production ML Systems

Model Deployment

LLM Applications

AI-powered Products

ML Evaluation & Tradeoffs

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

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.