AI Engineer

scalr; New York (NY); On-site; USD 200,000 - 300,000; Full time

Job summary

scalr in New York City is seeking an AI Engineer (Forward Deployed) to join a small, high-caliber AI team working at the frontier of model development and deployment. This role centers on hands-on Applied ML with a focus on LLM post-training, fine-tuning, and evaluation, driving real-world impact and production readiness.

The position emphasizes building robust evaluation, data curation, and deployment capabilities to push performance in live environments.

Qualifications

• Hands-on experience with LLM post-training and fine-tuning.
• Strong evaluation methodology and model quality.
• Experience with training infrastructure and debugging runs.
• Strong Python engineering fundamentals and ML systems.
• Ability to operate in fast-paced, ambiguous environments.

Responsibilities

• Fine-tune and iterate on LLMs
• Build and run evaluation frameworks to measure real performance
• Prepare and curate training data from messy, real-world inputs
• Debug training and inference issues, improving model reliability
• Support deployment of models into production environments

Skills

LLM fine-tuning

Evaluation frameworks

Training data curation

Debug training

Production deployment

Python engineering

Job description

AI Engineer (Forward Deployed)

NYC – $200k-$300k – early equity

Join a small, high-calibre AI team working at the frontier of model development and deployment.

This is a hands-on Applied ML role focused on LLM post-training, fine-tuning, and evaluation, adapting models for real-world use cases and pushing performance in production environments.

What You’ll Do:

• Fine-tune and iterate on LLMs
• Build and run evaluation frameworks to measure real performance
• Prepare and curate training data from messy, real-world inputs
• Debug training and inference issues, improving model reliability
• Support deployment of models into production environments

What We’re Looking For:

• Strong hands-on experience with LLM post-training and fine-tuning
• Deep understanding of evaluation methodology and model quality
• Comfortable with training infrastructure and debugging training runs
• Strong Python engineering fundamentals and experience shipping ML systems
• Ability to operate in fast-paced, ambiguous environments