Software Engineer, Research Acceleration (AI)

TL;DR

Software Engineer, Research Acceleration (AI): Building and optimizing research infrastructure for AI/ML with an accent on scalability, reproducibility, and high-throughput pipelines. Focus on designing systems for distributed evaluation, reward modeling, and multimodal assessment in a collaborative research environment.

Location: This role is based in San Francisco, California or New York, NY. We offer visa sponsorship and relocation support as needed.

Salary: $350,000–$475,000 USD annually

Company

hirify.global is advancing collaborative general intelligence by building AI products and open-source tools, including technologies like ChatGPT and PyTorch.

What you will do

• Design, build, and operate research infrastructure including evaluation frameworks, RL training systems, and experiment tracking platforms.
• Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.
• Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs.
• Partner directly with researchers to identify bottlenecks and unlock new capabilities, owning research tooling like a product manager.
• Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack.

Requirements

• Bachelor's degree or equivalent experience in computer science, engineering, or machine learning.
• Strong software engineering fundamentals with a track record of building reliable, maintainable systems.
• Proficiency in at least one backend language (Python or Rust).
• Comfort operating across the stack and owning projects end-to-end.
• Experience in highly collaborative environments involving cross-functional partners.

Nice to have

• Track record building tooling for researchers that achieved high adoption.
• Experience building or maintaining ML research infrastructure such as training frameworks or evaluation libraries.
• Contributions to open-source ML tools or widely-used internal frameworks.
• Familiarity with distributed systems, modern ML frameworks (PyTorch, JAX), and data processing at scale.
• Experience with research observability tools, distributed compute frameworks (Ray, Spark), or large-scale evaluation pipelines.

Culture & Benefits

• Generous health, dental, and vision benefits.
• Unlimited PTO and paid parental leave.
• Collaborative environment working directly with leading AI researchers.
• Focus on building systems that compound research velocity.