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Domain Expert – Python
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About the Role
This role is part of a project supporting leading LLM companies. The primary objective is to help these foundational LLM companies improve their Large Language Models. We support companies in enhancing their models by offering high-quality proprietary data. This data can be used as a basis for fine-tuning models or as an evaluation set to benchmark the performance. In an SFT data generation workflow, you might have to put together a prompt that contains code and questions, then elaborate model responses, and translate the provided CUDA/C++ code into equivalent Python code using PyTorch and NumPy to replicate the algorithm's behavior.
What You'll Do
- Translate CUDA/C++ code into equivalent Python implementations using PyTorch and NumPy, ensuring logical and performance parity.
- Analyze CUDA kernels and GPU-accelerated code for structure, efficiency, and function before translation.
- Evaluate LLM-generated translations of CUDA/C++ code to Python, providing technical feedback and corrections.
- Collaborate with prompt engineers and researchers to develop test prompts that reflect real-world CUDA/PyTorch tasks.
- Participate in RLHF workflows, ranking LLM responses and justifying ranking decisions clearly.
- Debug and review translated Python code for correctness, readability, and consistency with industry standards.
- Maintain technical documentation to support reproducibility and code clarity.
- Propose enhancements to prompt structure or conversion approaches based on common LLM failure patterns.
You're a Good Fit If You
- Required skills: PhD Maths
- 5+ years of overall work experience, with at least 3 years of relevant experience in Python and 2+ years in CUDA/C++.
- Strong hands-on experience with Python, especially in scientific computing using PyTorch and NumPy.
- Solid understanding of CUDA programming concepts and C++ fundamentals.
- Demonstrated ability to analyze CUDA kernels and accurately reproduce them in Python.
- Familiarity with GPU computation, parallelism, and performance-aware coding practices.
- Strong debugging skills and attention to numerical consistency when porting logic across languages.
- Experience evaluating AI-generated code or participating in LLM tuning is a plus.
- Ability to communicate technical feedback clearly and constructively.
Role Highlights
Pay & Payout
Independent contractor engagement through Turing. Apply via the link below - Turing handles vetting, onboarding and payment.
Common Questions
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