Senior Software Engineer, Applied AI

Flock Freight•Encinitas, CA

20h•Hybrid

About The Position

Flock Freight is looking for a Senior Software Engineer, Applied AI to help the company adopt AI in ways that produce measurable business impact. This is a hands-on, internal forward-deployed engineering role. You will embed with teams across the business, identify high-value workflows, build AI-powered agents, copilot experiences, and integrations, and help those solutions become part of how people work every day. The mandate is broader than reducing time to insight. You will help Flockers make better decisions, complete work faster, and improve operational outcomes. That may include enabling self-service access to data, embedding copilots into existing tools and workflows, automating repetitive operational work, or developing new agentic experiences where they can deliver meaningful ROI. You will own problems from discovery through production, delivery, and adoption. You will work closely with Platform Ops and business teams to understand real workflows, prototype quickly with users, iterate from feedback, support rollout and enablement, and measure whether the solution creates durable value. The goal is not to ship isolated demos. It is to build trusted AI capabilities that teams use, that improve business performance, and that can be reused across Flock. The secondary focus is contributing to Freight Factory automation as product roadmap priorities dictate. Both areas are product-driven. This role has optionality by design, with a natural path into broader product engineering over time.

Requirements

3 to 5+ years of software engineering experience, with at least 1 year delivering applied AI, ML, or intelligent automation in production.
Hands-on experience with LLMs such as OpenAI, Anthropic, Gemini, or similar, including prompt engineering, RAG, function and tool calling, evaluations, and agentic patterns.
Strong software engineering fundamentals, including system design, and a demonstrated ability to ship reliable production applications at high velocity.
Strong proficiency in Python and experience with APIs, data pipelines, integrations, and cloud-based application architectures.
Experience with Git and GitHub workflows, CI/CD, observability, and modern development practices.

Nice To Haves

Experience embedding with customers or internal business teams to discover workflows and deliver solutions in ambiguous problem spaces.
Experience driving adoption of new technical capabilities, including rollout, user enablement, feedback collection, and impact measurement.
Experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, or similar, or workflow orchestration tools.
Familiarity with enterprise AI platforms such as OpenClaw, Glean, Claude Cowork, or similar copilot-style tools.
Experience with agentic process automation or RPA platforms such as Sola.
Experience in logistics, freight, or other operations-heavy industries.
Stays close to the business and earns trust with technical and non-technical partners.
Comfortable moving between workflow discovery, hands-on coding, rollout, and measurement.
Uses AI tools across their own development workflow, not just builds them for others.
Stays current on the rapidly evolving landscape and forms strong opinions about what works.
Prefers shipping working solutions over theoretical frameworks.
Comfortable starting scrappy, learning from users, and iterating toward production quality.
Owns outcomes rather than outputs. Success means that people adopt the solution and the business sees measurable value, not simply that a tool was launched.
Communicates clearly with technical and non-technical audiences and can translate between business needs, user experience, and technical architecture.
As the first hire of this kind, brings a high degree of autonomy, helps the people around them work better, and builds patterns that scale across teams.
Comfortable in the freight world. Flock is not a typical SaaS company. The work is deeply operational, and the people you are building for, including scheduling coordinators, account managers, and operations teams, need practical solutions that improve their day-to-day work.

Responsibilities

Embed with business and operational teams to understand their goals, workflows, pain points, and constraints, then identify where AI can produce meaningful and measurable impact.
Translate ambiguous business problems into clear solution hypotheses, prototypes, production plans, and success measures.
Build side by side with end users and Platform Ops, incorporating feedback quickly and driving solutions from initial discovery through production adoption.
Own delivery end to end, including technical design, implementation, rollout, reliability, user feedback, and iteration.
Design and build AI agents, copilot experiences, and intelligent automations using LLMs, RAG, tool calling, agentic frameworks, and enterprise AI platforms.
Connect AI capabilities to enterprise data and systems, including Snowflake, operational databases, APIs, and internal platforms, with appropriate evaluation, observability, security, and feedback loops.
Build experiences that help employees query data, make decisions, and take action conversationally inside the tools and workflows they already use.
Contribute to Freight Factory automation as product roadmap priorities dictate.
Partner with Platform Ops and business leaders to support workflow redesign, rollout, training, and change management so new capabilities become part of day-to-day operations.
Act as a trusted technical partner to non-technical teams, helping them understand what AI can do, where it is appropriate, and how to use it effectively and responsibly.
Develop lightweight enablement materials, examples, and reusable playbooks that help teams adopt AI tools and identify additional high-value opportunities.
Create feedback channels with users and continuously improve solutions based on observed behavior, adoption barriers, and business outcomes.
Define success measures before building and instrument solutions to track adoption and business impact, including cost savings, hours saved, cycle-time reduction, quality, throughput, revenue impact, or other relevant operational outcomes.
Use impact and adoption data to decide when to iterate, scale, or stop an initiative.
Create reusable integration patterns, components, evaluations, and delivery playbooks that make future AI solutions faster and safer to deploy.
Codify lessons from embedded engagements and share them across Engineering, Platform Ops, and the broader business.
Help define the technical roadmap for applied AI within corporate systems with high autonomy and ownership.