AI Engineer (Synthetic Data Pipelines)

V7 At V7 we’re building AI platforms that help humans do their best work at incredible scale and speed.

Our mission is to turn human knowledge into trustworthy AI making complex tasks faster smarter and more accurate.

We’re growing fast backed by leading investors and AI pioneers (including the minds behind Transformers and Gemini).

The team you’ll be joining and the impact you’ll have We are a high-impact team at the forefront of AI research and engineering developing large-scale synthetic data generation pipelines to train cutting-edge machine learning models.

Our work blends rigorous experimentation with robust engineering bridging the gap between foundational research and production-quality systems.

We are seeking a technically strong and scientifically grounded AI Engineer to lead the development and evaluation of synthetic data pipelines used to train frontier models.

You will design modular reproducible pipelines that can be evaluated using proxy performance metrics while collaborating closely with researchers and ML practitioners.

The role requires strong command of experimental methodology comfort with ambiguity and fluency in large language model (LLM) systems—especially context engineering agentic execution strategies and performance optimization.

You will be expected to move quickly maintaining high-quality standards and leveraging modern AI tooling to streamline every stage of development.

What you’ll be doing from day one - Design implement and maintain synthetic data generation pipelines for multi-modal training tasks. - Evaluate pipeline output using well-grounded proxy metrics and sound statistical experiments. - Own the design and execution of experiments involving LLMs ensuring high reproducibility and clarity of findings. - Apply agentic design patterns and context engineering techniques to maximize model performance. - Use tools like Cursor GitHub Copilot and LLM agents to accelerate iteration debugging and documentation. - Collaborate with researchers and engineers across the stack to translate experimental insights into scalable systems.

Who you are - 3+ years of software engineering experience with at least one major programming language (Python or JavaScript preferred). - Strong academic background with an MS or higher in Computer Science Engineering Mathematics or a related scientific field. - Deep familiarity with Git DVC shell environments and data pipeline orchestration. - Solid foundation in statistics and experimental design especially in the context of ML evaluation. - Experience working with LLM systems including: - Prompt and context engineering - Agentic workflows - Output optimization and reliability strategies - Familiarity with recent research on LLM training datasets and evaluation benchmarks including: - CoDA: Agentic Systems for Collaborative Data Visualization https://scholar.google.com/scholar_url?url=https://arxiv.org/abs/2510.03194&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=72084023461291279&ei=AKoUadaFILmAieoP_rar8AY&scisig=ABGrvjIaTX_X2oxx8DyXgDJ1t66F - ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation https://scholar.google.com/scholar_url?url=https://arxiv.org/abs/2505.18668&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=17259409381983733182&ei=o6kUaaHYIrmAieoP_rar8AY&scisig=ABGrvjL7FbKBHumH_uQgU_wI-74b - Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data https://scholar.google.com/scholar_url?url=https://arxiv.org/abs/2503.16260&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=13999002578080941913&ei=D6oUabu9C6G7ieoP6LuqsQk&scisig=ABGrvjLCc9KxZM4kqAEdGhi5WvvG - ChartQA-X: Evaluation and Augmentation for Visual Chart Reasoning https://scholar.google.com/scholar_url?url=https://aclanthology.org/2022.findings-acl.177/&hl=en&sa=T&oi=gsb&ct=res&cd=0&d=10343579365391430110&ei=H6oUaduEG9ToieoPvZiZyAk&scisig=ABGrvjK_mfozL-JZmqZK2DDMECgM What We Value - Curiosity - A bias toward iteration and improvement—welcoming early feedback embracing failure as part of the discovery process and viewing feedback not as criticism but as a signal for the next meaningful step forward. - A structured and analytical mindset with strong attention to the scientific soundness of results. - The ability to thrive in fast-moving environments without clearly defined playbooks. - A preference for modular reproducible systems over ad-hoc experimentation. - Rigour in both code and evaluation especially when assessing LLM behaviour through proxy metrics and synthetic data feedback loops.

Why Join Us This is a rare opportunity to contribute directly to the next generation of training infrastructure for advanced AI systems.

The challenges are complex the tooling is bleeding-edge and the impact is tangible.

You will be surrounded by researchers and engineers who care deeply about both product and science and who are committed to solving hard problems with clear thinking and high standards.

V7 champions equality and inclusion because diverse teams build better products.

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