Applied AI Engineer

Jobzhr; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

MeeBoss is seeking an Applied AI Engineer in San Francisco to build systems that automate finance operations by deploying AI to operate computers, navigate browsers, process documents, and work through legacy systems for large enterprises.

The role focuses on browser agent reliability, document understanding, and inference optimization to improve accuracy and speed. Work spans cross-functional pipelines and production-grade deployments.

Qualifications

• Strong Python expertise and PyTorch proficiency.
• Experience shipping end-to-end ML systems.
• Comfort with messy data and turning it into usable data.
• Based in San Francisco or willing to relocate for in-person work 5 days a week.

Responsibilities

• Work on browser agent reliability and inference optimization.
• Push to state-of-the-art across UI interaction and unstructured data parsing.
• Build adaptive self-healing systems for changing environments.
• Design fine-tuning pipelines for customer-specific workflows.
• Optimize latency with model selection, quantization, caching, routing.
• Turn research into production systems.

Skills

Python

PyTorch

Clear communication

End-to-end systems

Messy data handling

Tools

PyTorch

Job description

About the job

MeeBoss is sharing this active opportunity on behalf of the hiring company. This role involves building systems that automate finance operations by deploying AI to operate computers like humans, navigating browsers, processing documents, and working through legacy systems for large enterprises. The position focuses on browser agent reliability, document understanding, and inference optimization to improve accuracy and speed.

Job title

Applied AI Engineer

Company

MeeBoss

Location

San Francisco, CA On-site

Compensation

$180,000-250,000/year

What you will do

• Work on browser agent reliability, document understanding, and inference optimization.


• Push to state-of-the-art across core automation capabilities including UI interaction, unstructured data parsing, and tool use.


• Build adaptive systems that self-heal when environments change.


• Design fine-tuning pipelines that learn from customer-specific workflows.


• Optimize latency across the stack through model selection, quantization, caching, and routing strategies.


• Turn research into production systems.

What we are looking for

• Strong Python expertise and proficiency in ML frameworks, particularly PyTorch.


• Eval-and-metric mindset, focusing on production metrics rather than just benchmarks.


• Comfort with messy data and the ability to make it useful.


• Track record of shipping end-to-end systems.


• Clear communication skills to describe work without buzzwords.


• Based in San Francisco or willing to relocate for in-person work 5 days a week.


• Experience with RL, retrieval systems, or agent-based systems (Nice-to-have).


• Cross-stack range including inference optimization, data pipelines, fine-tuning, and model monitoring (Nice-to-have).


• Published ML papers or significant OSS contributions (Nice-to-have).


• Lab or research exposure (Nice-to-have).


• Recent applied work on LLMs, browser agents, RAG, or production AI workflows (Nice-to-have).

About MeeBoss

MeeBoss helps job seekers and hiring teams make direct, relevant career connections.