Job Description
We are pioneering the data infrastructure for the year 2026 and beyond. As the demand for autonomous AI agents grows, so does the need for high-fidelity, privacy-preserving training data. Nexus Future Systems is seeking a visionary Senior Synthetic Data Architect to lead our initiative in building the world's most advanced generative data ecosystems.
In this pivotal role, you will design and implement autonomous data generation pipelines that simulate complex, real-world scenarios for our next-generation LLMs and autonomous driving systems. You will be at the forefront of the AI revolution, ensuring our models are trained on diverse, unbiased, and statistically perfect synthetic datasets.
Why Join Us?
We offer a competitive compensation package, equity packages, and the opportunity to work on projects that define the future of technology.
Responsibilities
- Architect and deploy scalable synthetic data generation pipelines using Python, PyTorch, and advanced generative models.
- Ensure data fidelity and statistical properties match real-world distributions to mitigate algorithmic bias in AI systems.
- Collaborate with cross-functional teams (ML engineers, Data Scientists, Product Managers) to identify data gaps and design synthetic solutions.
- Maintain rigorous data privacy standards and ensure compliance with evolving global regulations.
- Research and prototype emerging techniques in Generative Adversarial Networks (GANs) and Diffusion Models.
- Optimize data pipelines for speed, cost-efficiency, and reproducibility.
Qualifications
- 7+ years of experience in data engineering, machine learning, or a related technical field.
- Deep expertise in Python, PyTorch, TensorFlow, or equivalent data science frameworks.
- Strong understanding of statistics, probability, and data modeling principles.
- Proven experience working with Generative AI, GANs, or Diffusion models is highly preferred.
- Excellent problem-solving skills and the ability to work in a fast-paced, experimental environment.
- Experience with cloud infrastructure (AWS/GCP/Azure) and containerization (Docker/Kubernetes).