Research Engineer (AI)

Location: San Francisco, United States; hybrid work

Salary: $200,000–$240,000 per year, plus equity

Company

hirify.global develops AI-powered user interviews and digital twins that represent real people and reproduce their responses and behavior for qualitative research. The company serves enterprise customers and is backed by Silicon Valley investors.

What you will do

• Own evaluation infrastructure for testing competing modeling approaches, including fast setup, concurrent execution, automation, and monitoring.
• Implement deterministic and traceable measurement pipelines and maintain versioned evaluation datasets with held-out sets excluded from training.
• Build and maintain data pipelines for human-participant data, external datasets, and model-training data, including cross-source participant record linkage.
• Keep research code aligned with the shipped product and identify threats to evaluation validity.
• Track compute, API, and production-stage costs across experiments, including GPU provisioning across cloud providers.
• Manage repositories, code review, documentation, reproducibility, and improvements to the team's coding workflow.

Requirements

• Several years of software engineering experience, including experience supporting researchers in an academic lab or comparable setting.
• Experience translating scientific requirements and experiment designs into code.
• Experience building and operating large-scale data pipelines on human behavior data and verifying that they follow researcher-defined criteria and designs.
• Strong Python skills, including the ability to read and audit code produced by agentic coding tools.
• Knowledge of file systems, software libraries, databases, SQL, tabular data, and Git-based workflows.
• Experience with cloud infrastructure and running many jobs concurrently.
• Working knowledge of machine learning, transformer-based language models, model APIs, and fine-tuning.
• Experience engineering large language model systems, including prompt pipelines, evaluation harnesses, embedding computation, agentic systems, and/or fine-tuning infrastructure.
• Experience fine-tuning open-weight language models on multi-GPU hardware and serving them with an inference framework such as vLLM or SGLang, including batched inference and prefix caching.
• Experience with experiment tracking and cost monitoring for machine learning workloads.
• Experience with survey or panel data and data collection platforms.
• Experience extracting and storing model activations for downstream analysis.
• Prior work on generative agents, digital twins, or simulation of human respondents.
• A habit of writing deterministic, reproducible, cost-aware, and well-documented code.

Culture & Benefits

• Work in a tight-knit team serving large enterprise customers.
• Collaborate directly with scientists, with the role reporting to the Head Scientist.
• Participate in research involving AI-moderated user interviews, digital twins, and computational social science.
• Equity is included in the compensation package.