Job Description
At 2026, we are not merely predicting the future; we are architecting it. We are a frontier technology firm dedicated to building the predictive intelligence infrastructure that will define the next decade. Our mission is to transform raw data into actionable foresight, empowering industries to navigate the complexities of tomorrow.
We are seeking a visionary Senior AI Architect to join our elite engineering team. In this role, you will be at the helm of our core predictive modeling engine, designing systems that learn, adapt, and evolve. If you are passionate about pushing the boundaries of machine learning, deep learning, and generative AI, and you thrive in a fast-paced, high-impact environment, we want to meet you.
Why Join Us?
- Work on groundbreaking projects that redefine industry standards.
- Competitive compensation package and equity opportunities.
- Flexible remote-first policy with a collaborative San Francisco hub.
- Access to state-of-the-art hardware and research resources.
Responsibilities
- Design and implement scalable, high-performance machine learning pipelines and deep neural networks.
- Lead the architectural strategy for our predictive core, ensuring system reliability and scalability.
- Collaborate with data scientists and product managers to translate complex business requirements into technical solutions.
- Optimize existing models for latency and throughput, ensuring real-time performance in production environments.
- Conduct research into cutting-edge AI methodologies (e.g., Transformers, GANs, Reinforcement Learning) and evaluate their applicability to our products.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- 8+ years of professional experience in software engineering, with at least 4 years dedicated to AI/ML architecture.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Proven track record of deploying large-scale machine learning models to production.
- Excellent problem-solving skills and the ability to thrive in ambiguous, rapidly changing environments.