Job Description
Shape the future of intelligent systems with FutureScale 2026.
We are a pioneering research organization dedicated to defining the technological landscape of the next decade. Our 2026 Initiative focuses on developing next-generation generative AI models and autonomous infrastructure systems. We are looking for visionary Senior AI Engineers to join our elite team in San Francisco.
As a key player in this high-stakes project, you will bridge the gap between theoretical research and scalable production systems. You will work in a fast-paced environment where your code directly influences the trajectory of global technological evolution.
Why Join Us?
- Impactful Work: Build core components that will power the next generation of enterprise AI.
- Top-Tier Talent: Collaborate with PhDs, researchers, and industry veterans from top tech firms.
- Future-Ready: Focus on cutting-edge paradigms including Transformer architectures, Reinforcement Learning, and Edge AI.
Responsibilities
- Architect Development: Design and implement scalable, high-performance neural network architectures for our proprietary 2026 core models.
- Model Optimization: Apply techniques such as quantization, pruning, and distillation to deploy models efficiently on resource-constrained hardware.
- Research Integration: Translate theoretical research papers into production-ready code, iterating rapidly to improve model accuracy and latency.
- System Engineering: Build and maintain the MLOps pipeline, ensuring seamless CI/CD workflows for model training and deployment.
- Code Review & Mentorship: Lead code reviews, conduct technical workshops, and mentor junior engineers to foster a culture of excellence.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, with a focus on Machine Learning or Artificial Intelligence.
- Experience: 5+ years of professional experience in AI/ML engineering, specifically within a production environment.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and CUDA. Experience with distributed training frameworks (Ray, Horovod) is highly preferred.
- Language Model Expertise: Deep understanding of Large Language Model (LLM) mechanics, fine-tuning techniques (PEFT, LoRA), and RAG architectures.
- Problem Solving: Demonstrated ability to tackle complex, ambiguous problems with innovative technical solutions.