Job Description
Are you ready to engineer the future? Apex Innovations is seeking a visionary Lead Generative AI Architect to spearhead our next-generation AI initiatives. As we prepare for the transformative landscape of 2026, we are building systems that don't just process data—they think, create, and adapt. Join us to define the ethical and technical standards of next-generation intelligence.
The Role:
In this pivotal position, you will bridge the gap between theoretical machine learning breakthroughs and production-ready applications. You will lead a high-performance team in developing scalable, secure, and efficient Large Language Models (LLMs) and multimodal systems that drive our core business forward.
Why Join Us?
- Work on cutting-edge projects that will shape the industry in 2026 and beyond.
- Competitive compensation package including equity options.
- Flexible remote-first culture with a hub in the heart of San Francisco.
Responsibilities
- Architect and deploy large-scale generative AI models, ensuring high accuracy and low latency in production environments.
- Lead the end-to-end machine learning lifecycle, from data pipeline construction and model training to fine-tuning and evaluation.
- Collaborate with cross-functional teams (product, engineering, and compliance) to integrate AI solutions into customer-facing products.
- Establish best practices for MLOps, model governance, and responsible AI to mitigate bias and ensure safety.
- Mentor and grow a team of data scientists and ML engineers, fostering a culture of innovation and continuous learning.
- Stay ahead of the curve on emerging research in transformer architectures and neural rendering.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field; PhD preferred.
- 7+ years of experience in machine learning, deep learning, or NLP, with at least 3 years in a lead or architectural role.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of LLM architectures (GPT, BERT, LLaMA, etc.) and RAG (Retrieval-Augmented Generation) frameworks.
- Strong experience with cloud infrastructure (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Proven track record of deploying production models that handle high concurrency and large datasets.