Senior AI Research Engineer

C2R Ventures; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

C2R Ventures, located in New York, is seeking an elite Agentic AI Research Engineer to enhance their research and investment processes. The ideal candidate will build foundational infrastructure and develop robust data pipelines within a startup-like environment. Responsibilities include working with investment teams, designing a multi-agent AI framework, and optimizing data sets. Candidates should have strong Python skills and experience with LLMs, along with a proven track record in product development.

Qualifications

• Strong hands-on Python engineering skills to build robust infrastructure.
• Experience using LLMs with an emphasis on agentic harness design.
• Demonstrated success in taking a product from conception to completion.
• Understanding of LLM-as-a-judge, reward hacking, specification gaming.

Responsibilities

• Work directly with investment teams to find AI solutions to process bottlenecks.
• Design, build, and integrate a multi-agent AI framework for research.
• Create and optimize data sets for model training to maximize performance.

Skills

Python engineering

Experience with LLMs

Agentic harness design

Product development

Job description

Boutique Buy side firm with 20 year track record is seeking an elite Agentic AI Research Engineer to play a key role in the development and enhancement of their Research and Investment processes.

The ideal candidate will be a 0 to 1 builder who has developed complex agentic systems, ideally in a start-up environment.

You will be involved in building the foundational infrastructure and developing robust data pipelines through agentic design.

Ideal role for a candidate with a successful track record in the tech/start up industry and looking to pivot into the financial sector.

Responsibilities

• Work directly with investment teams (Quants, Researchers, etc) to find AI solutions to bottlenecks within their Research process.
• Design, build, and integrate a multi-agent AI framework that can orchestrate deep research across varying types of complex trading strategies.
• Create and optimize data sets for model training that maximize agent performance or ease of use on agentic tasks.

Requirements

• Experience using LLMs with an emphasis on agentic harness design
• Strong hands on Python engineering skills to build robust infrastructure
• Demonstrated success in the ability of taking a product from conception to completion
• Understanding of LLM-as-a-judge, reward hacking, specification gaming