About this role · Software development
About Turing:
Turing is one of the world’s fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.
Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Role Overview:
This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models.
One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models.
For example, for SFT data generation, you might have to put together or be provided a prompt which contains provided code and questions, you will then provide the model responses, and write corresponding Python code to solve the questions.
For RLHF data generation, you may need to create a prompt yourself or use one provided by the customer, ask the model questions, and evaluate the outputs generated by two versions of the LLM. You'll compare these outputs and provide feedback, which is then used to fine-tune the models. Please note that this role does not involve building or fine-tuning LLMs.
What does day-to-day look like:
- Design, develop, and maintain efficient, high-quality Python code to train and optimize AI models.
- Conduct evaluations (Evals) to benchmark model performance and analyze results for continuous improvement.
- Familiarity with Python frameworks and libraries
- Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria.
- Develop comprehensive explanations and rationales for evaluations, showcasing excellent reasoning and technical expertise.
- Lead efforts in Supervised Fine-Tuning (SFT), including creating and maintaining high-quality, task-specific datasets.
- Collaborate with researchers and annotators to execute Reinforcement Learning with Human Feedback (RLHF) and refine reward models.
- Design innovative evaluation strategies and processes to improve the model's alignment with user needs and ethical guidelines.
- Create and refine optimal responses to improve AI performance, emphasizing clarity, relevance, and technical accuracy.
- Conduct thorough peer reviews of code and documentation, providing constructive feedback and identifying areas for improvement.
- Collaborate with cross-functional teams to improve model performance and contribute to product enhancements.
- Continuously explore and integrate new tools, techniques, and methodologies to enhance AI training processes.
Requirements:
- 3+ years of strong experience with Python programming language.
- Industry experience and knowledge of code quality, formatting, and best practices of software development
- Experience with Python’s testing ecosystem, including unit, integration, and property-based testing.
- Knowledge of multi-threading and asynchronous programming in Python.
- Ability to work with architectural patterns and refactor code without introducing regressions.
- Strong debugging skills, including fixing memory and concurrency issues.
- Fluent in conversational and written English communication skills
Perks of Freelancing With Turing:
- Work in a fully remote environment.
- Opportunity to work on cutting-edge AI projects with leading LLM companies.
Offer Details:
- Commitments Required: at least 4 hours per day and minimum 20 hours per week with overlap of 4 hours with PST. (We have 3 options of time commitment: 20 hrs/week, 30 hrs/week or 40 hrs/week)
- Engagement type : Contractor assignment (no medical/paid leave)
- Duration of contract : 1 month; [expected start date is next week]
Evaluation Process (approximately 75 mins) :
- Two rounds of interviews (60 min technical + 15 min cultural &, offer discussion)
