Job Description
Join 2026 Labs, the premier hub for next-generation artificial intelligence. We are not just predicting the future; we are building it. As a Senior AI Architect, you will be at the forefront of the 2026 technological revolution, designing the neural networks and algorithms that will redefine human-machine interaction.
We are seeking a visionary leader to architect scalable, ethical, and groundbreaking AI systems. You will work directly with our R&D team to push the boundaries of what is possible in generative AI, reinforcement learning, and predictive modeling.
Why 2026 Labs?
- Shape the trajectory of AI technology for the next decade.
- Competitive salary and equity package in a high-growth unicorn environment.
- Work with the brightest minds in the industry in the heart of Austin.
Responsibilities
- Lead Architectural Design: Design and implement robust neural network architectures tailored for high-volume, low-latency production environments.
- Research & Innovation: Spearhead research initiatives to explore emerging AI paradigms relevant to the 2026 roadmap.
- Model Optimization: Continuously optimize models for accuracy, efficiency, and computational cost reduction.
- Mentorship: Guide a team of talented machine learning engineers and data scientists, fostering a culture of technical excellence.
- System Integration: Integrate complex AI models into our broader software ecosystem with seamless API design.
- Ethical Compliance: Ensure all AI implementations adhere to strict ethical guidelines and bias mitigation standards.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, or a related quantitative field.
- Experience: 7+ years of professional experience in machine learning engineering or applied AI research.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Ray, Spark).
- Domain Knowledge: Proven expertise in Natural Language Processing (NLP), Computer Vision, or Deep Reinforcement Learning.
- MLOps: Strong experience with MLOps pipelines, CI/CD for ML, and cloud infrastructure (AWS, GCP, or Azure).
- Communication: Excellent ability to translate complex technical concepts into strategic business value.