Applied AI Engineer

Pointer•San Francisco, CA

About The Position

At Pointer , we're building AI-native process automation to replace tedious human labor. We help companies map their processes, identify what's worth automating, and deploy AI agents that actually execute the work – across documents, browsers, and legacy systems. We recently raised our seed round from incredible investors (to be announced) and are looking for gritty, fast-learning engineers to join us. About the role You'll own how Pointer thinks – the intelligence that powers our automation. You'll work on everything from browser agent reliability to document understanding to inference optimization, building systems that make Pointer more accurate and faster every week. This is a role for someone who thrives on turning research into production.

Requirements

You’re strong in Python and ML frameworks, particularly PyTorch
You think in terms of evals and metrics and care about what they mean in production
You’re excited by messy data and figuring out how to make it useful
You like working across the stack: inference optimizations, data pipelines, fine-tuning, and model monitoring

Nice To Haves

You have experience with RL, retrieval systems, and agents
You've published ML papers or contributed to OSS projects

Responsibilities

Pushing Pointer to SOTA across our core automation capabilities (UI interaction, unstructured data parsing, tool use)
Building adaptive systems – self healing when environments change, judgment for ambiguous situations, and feedback loops that make every run smarter than the last
Fine-tuning pipelines that learn from customer-specific workflows and production edge cases
Latency optimization across the stack: model selection, quantization, caching, and routing strategies
Design and run experiments to improve Pointer’s performance and reliability