Staff Machine Learning Engineer — Production AI Systems

ActAI; Palo Alto (CA); On-site; USD 190,000 - 270,000; Full time

Job summary

ActAI in Palo Alto, CA, is seeking a Staff Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle—data, training, evaluation, inference, and deployment—in a hands-on leadership role at the intersection of research, systems, and product.

We value engineers who ship robust ML systems, optimize latency and resource use, and collaborate across teams to

Qualifications

• Ship ML systems in production, not just demos.
• Experience with large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.
• Experience operating GPU-based ML workloads at scale.
• Good technical judgment and independent problem-solving.
• Bias toward experimentation, measurement, and shipping.
• High standards for correctness and reliability.

Responsibilities

• Own end-to-end ML systems powering our company from data to deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems for capability, robustness, safety, and product performance.
• Architect high-performance inference systems optimizing latency, GPU utilization, memory, cost, and reliability.
• Build data pipelines for real-world and synthetic training data.
• Establish reliable production infrastructure for deploying, monitoring, and improving models.
• Collaborate with research and application engineering to turn model capabilities into product improvements.
• Make pragmatic trade-offs and iterate based on real-world performance.

Skills

ML systems design

Production-grade software

GPU-based training/inference

Python

Team leadership

Tools

PyTorch

JAX

TensorFlow

Job description

ActAI in Palo Alto, CA, is seeking a Staff Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle—data, training, evaluation, inference, and deployment—in a hands-on leadership role at the intersection of research, systems, and product.

We value engineers who ship robust ML systems, optimize latency and resource use, and collaborate across teams to