Job Description
Are you ready to define the technological landscape of 2026? Synthetix Future Labs is seeking a visionary AI Architect to lead our next-generation artificial intelligence initiatives. We are not just building software; we are architecting the future of human-machine interaction. If you have a passion for pushing the boundaries of what is possible with generative AI and large-scale neural networks, this is your opportunity to shape the trajectory of the industry.
Why join us?
- Work on cutting-edge projects that will define the tech stack of tomorrow.
- Competitive compensation package and equity options.
- Flexible remote-first culture with a collaborative hub in San Francisco.
- Access to state-of-the-art infrastructure and research resources.
We are looking for a leader who can bridge the gap between theoretical research and production-grade engineering.
Responsibilities
- Lead Architectural Design: Design and implement scalable, high-performance AI architectures capable of handling next-generation workloads and real-time data processing.
- Model Development: Spearhead the development and fine-tuning of large language models (LLMs) and generative AI systems focused on future-proof capabilities.
- Technical Strategy: Define the technical roadmap for AI initiatives, ensuring alignment with long-term business goals and industry standards for 2026.
- System Optimization: Optimize existing models for latency, throughput, and cost-efficiency in cloud environments.
- Mentorship: Guide a high-performing team of data scientists and engineers, fostering a culture of innovation and continuous learning.
- Collaboration: Partner with cross-functional teams including product managers, researchers, and security experts to integrate AI solutions seamlessly.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 7+ years of experience in software engineering, with at least 3 years specifically in AI/ML architecture and deep learning.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; experience with MLOps platforms (e.g., MLflow, Kubeflow); strong understanding of distributed systems.
- Domain Knowledge: Deep expertise in NLP, Computer Vision, or Generative Adversarial Networks (GANs).
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver robust, scalable solutions.
- Communication: Excellent verbal and written communication skills, with the ability to translate complex technical concepts for diverse stakeholders.