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# Staff/Senior Software Engineer

- **Company:** Jobot
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $185,000.0 - $235,000.0
- **Contract:** Permanent contract
- **Skills:** Business Logic, Knowledge-Based Systems, Large Language Models, Multi-Agent Systems, Build Management
- **Published:** September 11, 2026
- **Apply:** https://www.dice.com/job-detail/887d5f65-0273-4cd2-8a6f-4292b89929cb

## About the Role

We're looking for an experienced engineer who has shipped production LLM or agent systems beyond the prototype stage.

You should be comfortable diagnosing nondeterministic failures, establishing clear boundaries between model judgment and deterministic software, and determining when workflows require autonomous execution, human approval, or conventional application logic.

Ideal experience includes:

Production orchestration of stateful, long-running, and multi-agent workflows
Tool and action infrastructure with strong contracts, permissions, and approval boundaries
Retrieval, context engineering, evidence management, and persistent agent memory
Model-runtime engineering across multiple providers, latency profiles, and deployment environments
Evaluation, tracing, security, and operational control of nondeterministic AI systems
Designing reliable systems around LLM behavior in production environments, Experience deploying agent systems in government, defense, legal, healthcare, financial services, or other highly regulated environments
Familiarity with policy or legal research where temporal accuracy, provenance, and citation precision are critical
Experience operating self-hosted or multi-provider inference in restricted environments
Experience building agents that create durable artifacts or perform permissioned actions on behalf of users
Experience designing secure AI systems involving human-in-the-loop approvals and authorization boundaries
Working Here

## Description

We are seeking a Staff/Senior Software Engineer specializing in Agent Engineering to advance the agent runtime powering our platform's research, analysis, and action capabilities.

Our agents must be capable of decomposing open-ended objectives, retrieving trustworthy evidence, safely using tools, coordinating long-running work, and producing high-fidelity outputs that remain traceable to their sources.

You will own and improve the systems that make agent behavior reliable in production, including:

Execution state and orchestration
Tool use and permissions
Context and persistent memory
Model routing
Failure recovery
Evaluation and observability
Security and operational controls

The goal is to enable agents to perform meaningful work over extended periods without losing context, exceeding their authority, silently failing, or producing unsupported conclusions.

You will also advance the platform's reasoning and memory layer, enabling agents to accumulate knowledge, recognize changes, resolve contradictions, and maintain direct source context across workflows., Own and advance the platform's agent runtime and the foundational systems used to build new agent-driven products.
Build infrastructure that allows agents to manage long-running work from initial assignment through completion while keeping activity visible, steerable, and connected to relevant projects, people, evidence, deadlines, and deliverables.
Design and build the tool and action framework agents use to safely interact with internal systems and external services.
Connect agents to retrieval and knowledge systems so reasoning, memory, citations, and generated artifacts remain grounded in authoritative evidence.
Build and improve persistent memory and context-management systems for complex, multi-step agent workflows.
Strengthen evaluation, tracing, simulation, and release processes for agent behavior.
Improve agent quality and reliability while managing latency, inference cost, security, and operational risk.
Diagnose and resolve complex production failures involving nondeterministic AI systems.


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