AI/ML Engineer (AI)

Location: New York, NY; hybrid, in office Monday to Thursday. Candidates must live in or be able to commute to New York City.

Salary: $240K–$280K plus equity.

Company

hirify.global builds modern, AI-native application software and systems of record for U.S. federal agencies, including platforms for public services, procurement, and veterans' benefits.

What you will do

• Fine-tune, adapt, and distill open-weight models for requirements interpretation, code generation, repository understanding, structured output, and software validation.
• Curate datasets, build training pipelines, and run experiments across architectures and training techniques.
• Build evaluations on real software engineering tasks to establish baselines, expose failure modes, and distinguish meaningful gains from benchmark noise.
• Collaborate with platform engineers to identify model limitations and ship specialized capabilities into production.
• Apply supervised fine-tuning, synthetic data, preference optimization, and reinforcement learning to improve model behavior.
• Develop datasets, evaluations, and research practices supporting future model training from scratch while reducing frontier-model inference costs.

Requirements

• Hands-on experience training, fine-tuning, or adapting language models, including work with model weights rather than only model APIs or prompts.
• Strong software engineering fundamentals, including reliable training pipelines, complex-system debugging, and maintainable code.
• Strong Python and PyTorch skills, with working knowledge of transformer architectures, tokenization, and language-model training.
• A demonstrated model-engineering project, such as a fine-tuned model, original training approach, or meaningful open-source ML contribution; industry, research, or independent work counts.
• Do not apply if experience is limited to prompt engineering and API calls, if production deployment is not preferred, or if model cost is not considered.

Nice to have

• Experience fine-tuning models for code generation, program synthesis, or repository understanding.
• Experience designing experiments and evaluation sets that distinguish improvement from noise or overfitting.
• Experience with synthetic data, distillation, quantization, preference optimization, or reinforcement learning with verifiable rewards.
• Open-source model development, applied ML research, or reproduction of significant research results.

Culture & Benefits

• Full-time hybrid work with a New York City office, commuter benefits, expensed meals, and home-office setup support.
• Medical, dental, and vision coverage is fully paid for employees and dependents; life insurance, FSA, Dependent Care FSA, and a 401(k) with a 2% company match are provided.
• Paid parental leave, unlimited PTO with a two-week minimum, federal holidays, and a company-wide winter break.
• Gym or fitness reimbursement, utility, home cleaning, recreation, pet care, and professional-development stipends are available.
• Company offsites are held throughout the year.