Machine Learning Engineer (Platform)

Location: New York City, NY

Company

hirify.global builds AI-native operating systems for large, regulated institutions across government, insurance, health, construction, and financial services.

What you will do

• Build and own shared machine learning capabilities for document extraction, financial reporting, market data, and other institutional workflows.
• Develop Atlas platform components, including a foundation model for construction documents, extraction agents, model routing, and a unified evaluation system.
• Design continuous-improvement systems that capture production corrections, triage failures, support retraining, and enable safe redeployment.
• Compose vision models, VLM reasoning, LLMs, and rule engines into reliable composite AI systems.
• Turn urgent project-pod needs into reusable platform capabilities without over-abstracting before patterns are proven.
• Own capabilities end-to-end in production and collaborate with project pods and domain experts.

Requirements

• Strong understanding of machine learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
• Experience working with LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning.
• Ability to determine when to use fine-tuned segmentation models, vision-language models, rule engines, or composite systems.
• Platform engineering judgment: identify reusable capabilities while responding to real customer deadlines.
• Ability to engineer for production constraints involving accuracy, latency, cost, reliability, and messy real-world data.
• Ability to work from New York City, NY.

Culture & Benefits

• Work on applied AI systems deployed to production at national scale.
• Build products for regulated institutions where measurable customer impact is expected.
• Work at the research frontier with production stakes and problems that have no established playbook.
• Collaborate with an experienced engineering team from Palantir, Google, Meta, and Nvidia.
• Contribute learnings from individual projects to shared platform capabilities across the company.