Remote Quant Finance Analyst for AI Modeling

Turing Enterprises, Inc.; San Francisco (CA); Remote; USD 114,000 - 162,000; Part time

Benefits offered by this job

Fully remote

Flexible hours

Short-term contract

Job summary

Turing Enterprises, Inc. is seeking a Quantitative Finance-focused professional to work with researchers on improving AI model performance in finance.

You will apply quantitative modeling, statistical analysis, and financial engineering to evaluate and train AI systems, with no prior AI experience required. Day-to-day work includes evaluating models on stochastic finance topics, creating evaluation rubrics, and collaborating with researchers to shape training methods and benchmarks.

Qualifications

• 2+ years of experience in Quantitative Finance (e.g., quant trading, quant research, financial engineering, or risk modeling).
• Strong grasp of stochastic calculus, statistical modeling, derivatives pricing theory.
• Programming experience in Python, R, or C++.
• Excellent written English communication.

Responsibilities

• Evaluate LLM models on quantitative finance topics such as stochastic modeling, derivatives pricing, statistical arbitrage, and risk quantification.
• Create rubrics to assess model capabilities on tasks like options pricing, Monte Carlo simulation, factor model construction, and backtesting methodologies.
• Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation strategies, and benchmarks.

Skills

Quantitative Finance knowledge

English communication

Tools

Python

R

C++

Job description

Turing Enterprises, Inc. is seeking a Quantitative Finance-focused professional to work with researchers on improving AI model performance in finance.

You will apply quantitative modeling, statistical analysis, and financial engineering to evaluate and train AI systems, with no prior AI experience required. Day-to-day work includes evaluating models on stochastic finance topics, creating evaluation rubrics, and collaborating with researchers to shape training methods and benchmarks.