# Senior Software Engineer, Applied AI at Bot Auto

> United States · Senior · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Abotauto%3A5428954008 (read 2026-11-14).

## Facts (observed)

- Company: [Bot Auto](https://alderhq.app/companies/bot-auto)
- Location text: Houston, Texas (preferred), or the San Francisco Bay Area; Houston Office
- Where it can be done from (inferred): United States (evidence: "Location: Houston, Texas (preferred), or the San Francisco Bay Area; Houston Office")
- Posted: 2026-09-18 (57 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-12
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/botauto/jobs/5428954008
- Skills listed (inferred): TypeScript, JavaScript, Python, Docker, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Houston, n 8): p25 $148k, median $156k, p75 $166k

## Description (the employer's text)

### Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.

We are seeking a Senior Software Engineer, Applied AI to architect, build, ship, and operate production-grade AI and agentic systems across Bot Auto. This is an engineering-first role for a senior software engineer with proven experience building real AI agentic products or business workflows in production. You should be comfortable owning the system end to end across backend services, full-stack applications, databases, infrastructure, integrations, and modern AI/LLM engineering, while making sound decisions about where probabilistic AI should and should not be used.

### Key Responsibilities

- Own architecture and end-to-end implementation of production agentic products and workflows from problem definition through deployment and operation.
- Design boundaries between deterministic software, workflow engines, databases, source systems, and probabilistic agent reasoning.
- Build and review backend services, APIs, full-stack applications, and operator/user-facing tools supporting agentic workflows.
- Design agent orchestration, tool contracts, workflow state, memory, context, retrieval, approvals, escalation, retries, idempotency, rollback, and failure recovery.
- Design production data architecture, including relational schemas, state persistence, caching/search, event systems, and consistency behavior.
- Integrate AI systems with enterprise tools, internal APIs, data platforms, and operational systems while preserving permissions, auditability, and source-of-authority boundaries.
- Partner with infrastructure teams on cloud architecture, containers, CI/CD, queues, observability, tracing, security, scaling, latency, and cost.
- Establish reusable engineering patterns and reference implementations; work directly with users to convert high-value workflows into reliable products.
### Required Qualifications

- 6+ years of relevant professional software engineering experience, or equivalent demonstrated experience building and shipping production systems.
- Demonstrated hands-on experience architecting, building, shipping, and operating production-grade AI agentic applications, products, or business workflows.
- Strong backend and full-stack engineering capability across services, APIs, databases, application state, and user-facing web applications.
- Strong programming skills in Python and at least one modern application language such as TypeScript/JavaScript, or Go.
- Strong understanding of distributed systems, databases, data modeling, transactions/consistency, event-driven architectures, and production application design.
- Strong working knowledge of cloud infrastructure, containers, CI/CD, observability, security, and production operations.
- Deep practical understanding of LLM/agent systems, including tool calling, orchestration, context, retrieval, memory, workflow state, human review, guardrails, and failure recovery.
- Experience owning real production issues such as nondeterministic failures, stale context, tool errors, duplicate actions, permission failures, model regressions, latency, and cost trade-offs.
### Preferred Qualifications

- Experience with MCP, LangGraph, LangChain, LlamaIndex, or equivalent agent/tool orchestration frameworks.
- Experience with Temporal or another durable workflow engine for long-running, stateful workflows.
- Experience designing RAG, hybrid retrieval, vector search, graph/knowledge systems, or context-engineering architectures.
- Experience with eval-driven development, tracing, offline/online evaluation, model/prompt versioning, guardrails, and regression testing.
- Experience leading technical initiatives across teams; experience in autonomous systems, logistics, robotics, or other mission-critical domains is a plus.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Abotauto%3A5428954008 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.