Senior / Staff Engineer, Applied AI (Float Intelligence)

Float

4d•Hybrid

About The Position

Float is hiring a Senior or Staff Engineer to help build the foundation for an AI agent that serves as a financial assistant for Canadian businesses. The role is part of a small team developing the systems that connect model providers, Float product teams, and customers. This is a zero-to-one position involving the production launch and scaling of agents, the shared infrastructure they use, and the interfaces and channels customers use to reach them. The team expects approaches to evolve quickly and values learning fast and rebuilding when better methods become available. The engineer will build and scale the agent harness, including context and memory management, capabilities and tool use, evaluations, monitoring, observability, and distribution channels. The work includes making agent actions safe for financial use through permissions, approvals, audit trails, and clear handling of uncertainty or errors. The engineer will define and measure quality through offline evaluations and production monitoring, and help product teams launch agent capabilities on shared infrastructure. At Staff level, the engineer will establish patterns for the harness and set evaluation and observability standards for agents across Float. The position is suited to engineers who have shipped agents to real users and want substantial ownership. Float is hiring at multiple levels and will determine level based on scope, judgment, and learning velocity.

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.