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Applied AI Engineer
Ersilia
5 hours ago
Full-time
On-site
San Francisco, California, United States
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
Strong Python and ML frameworks, particularly PyTorch.
Eval-and-metric mindset. You think in terms of metrics that matter in production, not just benchmarks.
Comfort with messy data and figuring out how to make it useful.
Track record of shipping. You can describe specific systems you've built end-to-end.
Crisp communication about your own work. You can describe what you built in a few clear sentences without buzzwords.
Based in San Francisco or willing to relocate, in-person 5 days a week.
Nice-to-Have
Experience with RL, retrieval systems, or agent-based systems
Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring
Published ML papers or significant OSS contributions
Lab or research exposure (SAIL, BAIR, MIT CSAIL, similar)
Recent applied work on LLMs, browser agents, RAG, or production AI workflows
#J-18808-Ljbffr
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
Strong Python and ML frameworks, particularly PyTorch.
Eval-and-metric mindset. You think in terms of metrics that matter in production, not just benchmarks.
Comfort with messy data and figuring out how to make it useful.
Track record of shipping. You can describe specific systems you've built end-to-end.
Crisp communication about your own work. You can describe what you built in a few clear sentences without buzzwords.
Based in San Francisco or willing to relocate, in-person 5 days a week.
Nice-to-Have
Experience with RL, retrieval systems, or agent-based systems
Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring
Published ML papers or significant OSS contributions
Lab or research exposure (SAIL, BAIR, MIT CSAIL, similar)
Recent applied work on LLMs, browser agents, RAG, or production AI workflows
#J-18808-Ljbffr