Senior Software Engineer II, Applied AI

LVT•Seattle, WA

12d

About The Position

LVT is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first mobile, solar-powered units, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions—from our physical units in the field to a powerful Agentic AI platform—that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations. This is your chance to join a cutting-edge team that isn't just watching the world change, but actively building the technology that is changing it. We’re a team that’s focused on growth and innovation, and we’re proud that our crew, products, and leadership are being recognized for it. A Top-Tier Growth Company: Named one of the Financial Times’ Fastest Growing Companies 2025 and #10 on the Inc. 5000 Rocky Mountain Regional list for 2025. Innovative Leadership: Our CEO, Ryan Porter, was named an EY Entrepreneur of the Year 2025, and our CTO, Steve Lindsey, was inducted into the Silicon Slopes CTO Hall of Fame in 2024. Product & Software Excellence: We were named one of The Software Report’s Top 100 Software Companies of 2023 and are a winner of the Security Today Govies Award for 2025. We are seeking a Senior Software Engineer, Applied AI to own end-to-end delivery of LVT's GenAI-powered security deterrence product. It sits directly on top of LVT's perception stack and turns detections into spoken action in the real world. Where our AI platform and data teams build the rails, this role builds the product that rides on them and is accountable for production delivery. You will own both the GenAI harness and the application code around it, from design through production and iteration. Because this product acts on the physical world, you'll own the precision and safety bar that comes with it. This is a hands-on senior individual-contributor role at the intersection of several functions. You'll work cross-team with backend and platform engineers, with the ML/LLM Ops platform you deploy against, and directly with the ML engineers who own the detection models, integrating their work into a shipped product and feeding field behavior back to them. You'll also own pragmatic build-versus-buy decisions for this product: when to self-host versus call a managed model, which voice/TTS approach to adopt, and where to draw the line between product-specific code and shared platform capabilities. You should be equally comfortable writing production application code, designing an evaluation harness for a system that must rarely act wrongly, integrating against detection models you don't own, and making a defensible build-versus-buy call under real cost and latency constraints.

Requirements

6+ years building and shipping production software, backend or full-stack with strong systems and API design judgment and ownership of services in production.
Has built products on top of ML/GenAI models including orchestration, prompting, retrieval or tool-calling, and especially generation such as voice/TTS including the evaluation harness needed to keep a non-deterministic system reliable. This is application of models in a product, not model training.
Experience with systems where a wrong output has real consequences real-time, event-driven, or actuating systems and the precision and latency discipline that requires. (security, robotics, IoT, or safety-relevant systems are all relevant)
A track record of owning a product or major feature end to end, not just implementing a spec handed down.
Effective working directly with ML scientists and across platform, data, and product teams; comfortable integrating against models and contracts owned by others.
Demonstrated pragmatic technology selection under cost and latency constraints, with the reasoning made explicit.
Strong Python, plus the application stack in use (e.g. TypeScript/Node); experience building APIs and production backend systems.
Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Computer-vision / video products, especially detection-driven systems (person/vehicle detection, low-light imaging).
Voice / TTS generation and customization at production quality.
Agentic and generative tooling: LangGraph, MCP frameworks, vector databases, and inference/serving platforms.
Experience building evaluation and regression frameworks for non-deterministic or real-world-actuating systems.
Familiarity with edge-to-cloud or IoT systems.

Responsibilities

Own GenAI-powered security product from design through production and field iteration including architecture, application code, rollout, monitoring, and the follow-through after launch.
Build and own the logic that turns a detection into the right spoken response including orchestration, prompting, voice generation and customization with a product-level evaluation and regression suite that catches false or mistimed talkdowns before they reach the field.
Own the false-positive and timing discipline a real-world-actuating system demands. Define what "acted correctly" means, instrument it across lighting and scene conditions, and hold the bar as detection types expand.
Design and build the services, APIs, and integration code that wrap detection and voice into a product, to LVT's standards for reliability, observability, and operational readiness.
Integrate against the ML/LLM Ops serving platform and the data team's datasets and contracts rather than rebuilding them, and partner with the ML scientists who own the detection models turning their models into product behavior and routing field signals back to them.
Own build-versus-buy recommendations and decisions for this product's components managed model API versus self-hosted, voice/TTS provider versus in-house, third-party framework versus shared platform capability with cost, latency, and maintenance trade-offs made explicit.