LLM Algorithm Engineer

Menlo Park, CA · 5 days/week in office · Full-time About Corepass We operate at the intersection of frontier model research and production-grade applied AI. Our customers are not looking for demos. They need agents that drive revenue, support underwriting decisions, and perform reliably in real business environments. We are early, well-resourced, and moving fast. The role We are looking for a Large Language Model Algorithm Engineer to help build, fine-tune, align, optimize, and deploy large language models for real-world AI systems. This role focuses on model training, post-training, domain-specific model development, evaluation, inference optimization, and integration into agent systems. Responsibilities Own fine-tuning, post-training, and performance optimization for large language models. Build domain-specific LLMs, including data design, training, and evaluation. Lead model alignment efforts, including SFT, DPO, RLHF, and related data strategy optimization. Explore reinforcement learning applications in large language models, including PPO, actor-critic methods, and related approaches. Build an end-to-end model iteration loop, covering data, training, evaluation, and continuous improvement. Contribute to model inference optimization, including distillation, quantization, acceleration, and deployment. Support the practical deployment of models in agent-based systems. Requirements Master’s degree or above in computer science, mathematics, artificial intelligence, or a related field. Strong foundation in deep learning and reinforcement learning. Solid understanding of Transformer architecture and large-scale model training workflows. Experience with LLM fine-tuning or post-training, such as SFT, DPO, or RLHF, is preferred. Proficient in PyTorch and familiar with Linux and GPU-based development environments. Strong modeling ability and engineering implementation skills. Strong sense of ownership and a continuous improvement mindset. Why Corepass Foundational work: Join an early team building both model infrastructure and applied AI products. Real product focus: We train our own models and build agents designed for production use, not demos. High-impact problems: Our agents are built for business-critical workflows such as lead generation and underwriting. Fast execution: We are a tight team with low bureaucracy and a strong bias toward shipping. In-person culture: We work from our SF Bay Area office five days a week. Corepass is an equal opportunity employer. We hire on merit and welcome candidates of every background. #J-18808-Ljbffr