Ai Engineer Jobs

About The Position

Requirements

• Experience building and shipping agents or LLM-backed systems to production, and monitoring, improving, and scaling them.
• Typically 5+ years of software engineering experience for Senior level or 8+ years for Staff level.
• For Staff level, a track record of technical leadership across teams.
• Depth in either full-stack product engineering for agentic experiences or backend engineering for LLM pipelines and agent infrastructure.
• Hands-on, current knowledge of frontier models, inference, and context management, with informed opinions about what works.
• Comfort giving agents capabilities and context and observing their behavior rather than specifying every step.
• Experience at more than one company stage, or at a rapidly growing company, with significant ownership and impact.
• A habit of proactively building things and seeing them through to completion.
• Curiosity and willingness to restart or replace approaches when the field changes.
• Customer focus, ownership, adaptability, continuous learning, high standards, and collaborative communication.

Nice To Haves

• Experience as a founder, founding engineer, or early engineer on a major initiative within a larger company.
• Experience building systems that move money or support accountable decisions, such as fintech, payments, or other regulated products.
• Experience designing evaluation frameworks for LLM systems, including LLM-as-judge and human-in-the-loop labeling.

Responsibilities

• Ship agents that save customers time and money, from prototype through production at scale, and own their outcomes after launch.
• Build the shared agent harness, including context management, capabilities and tool use, evaluation, monitoring, and observability.
• Develop safeguards for agent actions involving company finances, including permissions, approvals, audit trails, and clear behavior when agents are uncertain or wrong.
• Define quality standards and measurement through offline evaluations, production monitoring, and improvement loops informed by incidents.
• Build the interfaces and channels through which agents are delivered, including chat and generative UI.
• Partner with product teams so they can launch agent capabilities using the shared harness without rebuilding the infrastructure.
• Track developments in models, inference, and context management, and revise existing systems when better approaches emerge.
• At Staff level, set the patterns for the harness and the evaluation and observability standards for agents across Float.

Benefits

• Competitive compensation
• Equity options
• Benefits
• Catered team lunches on Tuesdays, Wednesdays, and Thursdays
• Dog-friendly office