Machine Learning Engineer (Applied AI)

Location: New York City, NY

Company

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

What you will do

• Turn ambiguous institutional problems, limited data, and undefined success criteria into well-posed machine learning problems and production systems.
• Build and own AI systems end to end, including computer vision pipelines, segmentation models, VLM reasoning, rule engines, LLM applications, and agentic systems.
• Develop multimodal document-understanding models for blueprints, site plans, policy stacks, contracts, and clinical records.
• Design training loops, reward signals, evaluation suites, and failure-mode taxonomies using verified outcomes from real deployments.
• Work directly with permit reviewers, underwriters, and compliance officers to ensure systems improve real institutional workflows.
• Engineer for production constraints including accuracy, latency, cost, reliability, and distribution shift.

Requirements

• Strong understanding of machine learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
• Hands-on experience with modern AI systems, including LLMs, prompting, fine-tuning, tool use, and reasoning.
• Ability to choose and compose models, including fine-tuned segmentation models, vision-language models, and rule engines.
• Ability to work with underspecified problems, difficult multimodal documents, and evolving definitions of success.
• Ability to own model behavior from training through production deployment.
• Based in New York City, NY.

Nice to have

• Experience with reinforcement learning fine-tuning and agents that learn from verified production outcomes.
• Experience building institutional-grade evaluation systems and failure-mode taxonomies.

Culture & Benefits

• Work on frontier applied AI problems with direct production and customer impact.
• Every project is expected to ship to production and create measurable value.
• Collaborate with an experienced engineering team with backgrounds at Palantir, Google, Meta, and Nvidia.
• Contribute through design reviews, an internal paper club, and a shared playbook for trustworthy AI systems.