ML Engineer | Remote

CodeGeniusRecruit; United States; On-site; USD 413,000 - 551,000; Full time

Job summary

CodeGeniusRecruit is seeking a seasoned machine learning engineer to tackle production ML challenges. You will work on model training, inference systems, MLOps, and LLM applications using coding agents to boost developer productivity.

You will review model-generated implementations, identify bugs and performance issues, compare outputs across models, and apply engineering judgment to real-world ML scenarios, delivering robust production solutions.

Qualifications

• Strong experience in machine learning engineering.
• Strong experience building production ML systems, model deployment infrastructure, LLM applications, or intelligent software products.
• Strong experience using coding agents and developer productivity tools.
• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
• Strong experience deploying machine learning systems to production.

Responsibilities

• Complete and evaluate complex machine learning engineering tasks using coding agents.
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and system failure modes.
• Compare outputs from multiple models and assess strengths and weaknesses.
• Apply engineering judgment to real-world machine learning engineering scenarios.

Skills

ML engineering

Production ML

LLM applications

Coding agents

Model deployment

Debugging

Tools

Developer productivity tools

Job description

Commitment:

Sprint-based project running in 12 24 hour stretches based on project requirements

Commission:

$85/hour (Typical accepted tasks pay approximately $400 and take 2 3 hours after ramp-up).

What You'll Be Doing

• Complete and evaluate complex machine learning engineering tasks using coding agents.
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and system failure modes.
• Compare outputs from multiple models and assess strengths and weaknesses.
• Apply engineering judgment to real-world machine learning engineering scenarios.

What We Re Looking For

• Strong experience in machine learning engineering.
• Strong experience building production ML systems, model deployment infrastructure, LLM. applications, or intelligent software products.
• Strong experience using coding agents and developer productivity tools.
• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
• Strong experience deploying machine learning systems to production environments.