Applied AI Engineer

Ersilia; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Ersilia seeks an Applied AI Engineer in San Francisco to advance production AI systems, focusing on browser agents, document understanding, and latency optimization. You will turn research into robust, scalable features that improve accuracy and speed, requiring hands-on execution and strong cross-functional collaboration.

The role emphasizes end-to-end system delivery, reliable pipelines, and clear communication of results to stakeholders.

Qualifications

• Proficiency in Python and ML frameworks with emphasis on PyTorch.
• Ability to define and measure production-relevant metrics.
• Experience handling messy, real-world data.
• Track record shipping complete ML systems end-to-end.
• Clear communication of complex work without buzzwords.

Responsibilities

• Build production-grade AI systems including browser agents and document understanding modules.
• Improve reliability and latency across the ML stack through optimization, tuning, and monitoring.
• Collaborate with researchers and engineers to translate research into production features.
• Communicate progress and outcomes with non-technical stakeholders.

Skills

Python

PyTorch

ML frameworks

Metrics mindset

End-to-end shipping

Communication

Job description

About the Company

We deploy AI that operates computers the way humans do: navigating browsers, processing documents, working through legacy systems. The company works with large enterprises to automate their messiest finance operations, attacking the $300B+ BPO industry built on labor arbitrage that software couldn't touch because people were the product.

About the Role

As an Applied AI Engineer, you'll work on everything from browser agent reliability to document understanding to inference optimization, building systems that make the work more accurate and faster every week. This is a role for someone who thrives on turning research into production, working on problems like pushing to state-of-the-art across core automation capabilities (UI interaction, unstructured data parsing, tool use), building adaptive systems that self-heal when environments change, designing fine-tuning pipelines that learn from customer-specific workflows, and optimizing latency across the stack through model selection, quantization, caching, and routing strategies

Requirements Must-Have

1. Strong Python and ML frameworks, particularly PyTorch.
2. Eval-and-metric mindset. You think in terms of metrics that matter in production, not just benchmarks.
3. Comfort with messy data and figuring out how to make it useful.
4. Track record of shipping. You can describe specific systems you've built end-to-end.
5. Crisp communication about your own work. You can describe what you built in a few clear sentences without buzzwords.
6. Based in San Francisco or willing to relocate, in-person 5 days a week.

Nice-to-Have

1. Experience with RL, retrieval systems, or agent-based systems
2. Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring
3. Published ML papers or significant OSS contributions
4. Lab or research exposure (SAIL, BAIR, MIT CSAIL, similar)
5. Recent applied work on LLMs, browser agents, RAG, or production AI workflows