Job Description
We are 2026, a pioneering research lab dedicated to building the infrastructure for artificial general intelligence. We are looking for a visionary Lead AI Architect to design the neural architectures that will power the next generation of autonomous systems. If you are passionate about the intersection of theoretical computer science and practical engineering, this is your opportunity to shape the future.
Why Join Us?
- Work on cutting-edge projects that redefine human-computer interaction.
- Competitive compensation and equity packages.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to publish research and influence the industry.
Role Overview:
The Lead AI Architect will be responsible for the end-to-end design of our machine learning systems. You will lead a team of engineers in developing scalable, robust, and ethical AI models. Your work will directly impact how we solve complex problems in automation and decision-making.
Responsibilities
- Architect and implement scalable deep learning pipelines for large-scale data processing.
- Lead the technical strategy for model optimization, including fine-tuning and inference acceleration.
- Mentor junior engineers and researchers, fostering a culture of innovation and technical excellence.
- Collaborate with product managers to define AI requirements and translate them into technical specifications.
- Stay abreast of the latest advancements in NLP, Computer Vision, and Reinforcement Learning.
- Ensure model robustness, fairness, and compliance with industry standards.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- 7+ years of professional experience in machine learning engineering or research.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Proven track record of deploying production-grade ML models.
- Excellent communication skills and ability to present complex technical concepts to non-technical stakeholders.