Principal Machine Learning Engineer (AI)

TL;DR

Principal Machine Learning Engineer (AI): Architecting and evolving critical ML systems including training, inference, and evaluation infrastructure with an accent on large-scale model performance and GPU efficiency. Focus on solving complex architectural challenges, building reproducible pipelines, and ensuring system reliability at scale.

Location: Must be based in Palo Alto, California (Hybrid role)

Company

A builder of proactive AI systems that understand context, plan actions, and execute work over time.

What you will do

• Architect and build large-scale ML systems spanning data, training, evaluation, and inference.
• Design reproducible, high-performance training pipelines on GPU infrastructure.
• Architect inference systems that balance latency, throughput, cost, and reliability.
• Implement evaluation pipelines covering model robustness, safety, and bias.
• Own production deployment including GPU optimization and memory efficiency.
• Collaborate with product teams to integrate ML systems into user-facing applications.

Requirements

• Strong background in deep learning and transformer-based architectures.
• Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
• Proficiency in at least one modern framework like PyTorch or JAX.
• Experience with distributed training frameworks such as DeepSpeed, FSDP, or Ray.
• Solid software engineering fundamentals for building production-grade systems.
• Knowledge of GPU optimization, memory efficiency, and mixed precision.

Nice to have

• Experience with LLM inference frameworks like vLLM or TensorRT-LLM.
• Background in scientific computing, compilers, or GPU kernels.
• Experience with RLHF pipelines (PPO, DPO).
• Experience training or deploying multimodal models.

Culture & Benefits

• High talent density, hands-on environment with a small, world-class team.
• Fast-paced, collaborative decision-making culture.
• Opportunity to work on zero-to-one AI systems with global scale and impact.

Hiring process

• Evaluation by technical team members.
• 3 to 4 interviews conducted via virtual meetings or onsite.
• Transparent and efficient process with prompt decision-making.