Senior AI Engineer - Machine Learning - Frontier Industrial AI Startup

About this role

Senior AI Engineer - Machine Learning - Frontier Industrial AI Startup

Palo Alto, CA (you can work from home 3x/week)

The company has about 70 employees and a team of 10 AI Scientists and Engineers.

The company was founded and funded with a very large seed round from one of the leading companies in its massive industry. The company's industrial AI software uses machine learning to optimize manufacturing processes, improve quality, and increase production efficiency.

In this position you would be developing and deploying large-scale machine learning systems, with a focus on pretraining, fine-tuning, and serving tabular foundation models. You would be building and optimizing ML infrastructure, including data pipelines, distributed training, inference, deployment automation, and model monitoring. You would collaborate with AI researchers and engineering teams to turn cutting-edge ML research into scalable, reliable production systems while leading technical architecture and mentoring engineers.

The company will pay a base salary up to $250k, plus yearly bonuses bringing yearly cash compensation up to $300k, plus stock options which could be lucrative.

Job Responsibilities:

• Partnering with AI Scientists to build and productionize our tabular foundation models — owning the pretraining, fine-tuning, serving, and monitoring infrastructure and scaling it from research prototype to large-scale production.

• Building reliable, performant ML infrastructure across research, staging, and production: data, training, and inference pipelines, CI/CD, observability, and reproducible workflows, tuned for latency, throughput, and resource usage.

• Designing evaluation and monitoring that reflect how models are actually used, including handling real-world data challenges such as distribution shift and limited labels.

• Setting engineering standards, leading design and architecture reviews, and driving adoption of new modeling approaches, algorithms, and infrastru...