xAI is looking to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge by driving the evolution of AI models' reasoning capabilities through inventive post-training approaches.
Requirements
- Post-Training Specialization: Extensive hands-on experience with post-training techniques (e.g., RLHF, DPO, or alignment methods) focused on reasoning improvements, with a portfolio of successful model enhancements.
- Creative Problem-Solving Prowess: Demonstrated ingenuity in solving ambiguous AI challenges, evidenced by innovative projects, research contributions, or unconventional approaches that yielded breakthroughs.
- Broad Technical Foundation: Proficiency in AI engineering tools (e.g., Python, PyTorch) and a solid grasp of model architectures, enabling you to navigate wide-ranging tasks from ideation to deployment.
- Research Versatility: Background in AI research with emphasis on reasoning, logic, or cognitive science, including publications or experiments that highlight creative applications in post-training contexts.
- Adaptable Expertise: 3+ years in multifaceted AI roles at cutting-edge organizations, where you've thrived in broad scopes by balancing creativity with rigorous execution to deliver high-impact results.
Responsibilities
- Post-Training Optimization: Apply and innovate on post-training methods like fine-tuning, reinforcement learning variants, or data augmentation to sharpen reasoning skills, ensuring models deliver more accurate, coherent, and insightful outputs.
- Creative Methodology Design: Invent novel strategies for enhancing reasoning, such as custom prompting frameworks, synthetic reasoning datasets, or hybrid techniques that push the limits of model cognition through out-of-the-box thinking.
- Problem-Solving Exploration: Tackle broad reasoning challenges creatively, from debugging logical inconsistencies to engineering solutions for edge-case scenarios, using iterative experimentation to uncover hidden potential in models.
- Evaluation and Iteration: Develop creative benchmarks and metrics to assess post-training impacts on reasoning, enabling rapid cycles of refinement that align with evolving user needs and technological frontiers.
- Collaborative Innovation: Partner with diverse teams to integrate post-training advancements into our AI ecosystem, leveraging your creativity to inspire cross-pollination of ideas and accelerate overall model intelligence.
Other
- Work ethic and strong prioritization skills are important.
- All engineers and researchers are expected to have strong communication skills.
- Candidates are expected to be located near Palo Alto or open to relocation.
- We hire engineers in Palo Alto. Our team usually works from the office 5 days a week but allow work-from-home days when required.
- Leadership is given to those who show initiative and consistently deliver excellence.
