Forward Deployed Engineer

Hybrid — Denver, CO
Full-time
25–50% travel

We’re looking for a Forward Deployed Engineer (FDE) to accelerate the application of AI in real-world environments by embedding with customers and partners to deliver high-impact deployments that drive measurable customer value.

You’ll own end-to-end delivery of customer outcomes using AI systems and edge technologies, working across hardware, networking, sensors, data systems, and AI. You’ll integrate deeply into customer environments to compress adoption cycles, iterate based on real needs, and capture product enhancements that improve future deployments.

This role operates at the edge of the platform rather than inside its core. You’ll have authority over field deployment and configuration decisions through controlled execution and release mechanisms.

This is a hands-on role with 25–50% travel for someone who thrives in a high-ownership environment and wants to build and deploy the infrastructure that makes real-world AI possible.

• Own the design, deployment, and iteration of customer-facing solutions from initial problem definition through production rollout, adoption, and expansion.

• Deliver measurable outcomes by traveling to customer sites 25–50% of the time and working closely with customer teams and embedded partner teams.

• Deploy and operate systems in real-world environments, including edge hardware, networking, connectivity, sensors, data flows, and AI inference.

• Build integrations and tooling to connect customer systems, including OT/IT environments, data sources, and workflows, into production-grade applications.

• Diagnose issues across the full stack—from hardware and networking through data, applications, and AI—and respond quickly to resolve deployment and production challenges.

• Capture deployment learnings and translate them into repeatable playbooks, scalable patterns, and product enhancements.

• Use AI-native workflows and tools to accelerate drafts, implementations, documentation, and other delivery artifacts, then refine outputs using engineering judgment.

• Communicate clearly with technical and non-technical stakeholders and lead working sessions that drive decisions and execution.

• Taken full ownership of at least one customer account and delivered a clear, measurable improvement in customer value.

• Built strong context on customer constraints and operational realities and used it to make sound trade-offs across product, systems, and delivery.

• Earned trust through autonomy, responsiveness, and high-quality execution, becoming someone teams rely on to move deployments forward.

• Independently owning multiple deployments or a strategic account end-to-end, consistently compressing adoption cycles and driving measurable outcomes.

• Creating repeatable deployment patterns that reduce delivery friction and improve speed-to-value across customers and partners.

• Feeding a steady stream of product improvements back into Engineering, helping evolve the platform based on real-world constraints and deployment learnings.

• 5+ years building and operating production software or systems, ideally in customer-facing or delivery-oriented roles.

• Experience working in complex environments across infrastructure, networking, security, data systems, and/or production AI.

• Familiarity with technologies such as Linux, Docker/Kubernetes, REST/gRPC APIs, and modern observability tooling.

• Strong engineering craft, including clean implementations, thoughtful designs, operational clarity, and strong documentation.

• Experience with Python and/or Go, shell scripting, and reliable integration practices.

• Comfort working in ambiguous environments and making sound trade-offs when timelines and constraints are real.

• Clear communicator and strong collaborator across technical and non-technical stakeholders.

• Ownership mindset focused on outcomes rather than tasks, with a natural tendency to take responsibility for delivery, adoption, and customer value.

• Delivering complex deployments end-to-end and turning one-off wins into repeatable patterns, such as deployment playbooks, runbooks, and reusable configuration templates.

• Hands-on experience with edge or hybrid systems across hardware, networking, connectivity, and containerized software environments.

• Familiarity with VPNs, TLS, firewalls, LTE/5G/Wi-Fi, and Kubernetes deployments.

• Deep comfort diagnosing and solving cross-stack issues in production environments, including logs, metrics, traces, packet capture, network debugging, and performance profiling.

• Production experience applying modern AI systems, including LLMs, agents, or inference, in customer workflows under real constraints involving latency, bandwidth, reliability, and security.

• Experience embedding with customer and partner engineering teams to align multi-party delivery toward measurable outcomes.

• Work in a high-ownership, real-world startup environment where you can move quickly, build new systems, and see your impact directly through solutions operating in the field.

• Use modern AI tools throughout development, documentation, planning, troubleshooting, and deployment workflows to accelerate execution.

• Take on challenging technical problems across next-generation cloud and IoT, hardware/software/networking, edge environments, data and AI inference, and secure systems operating in demanding OT settings.

• Learn quickly by working with exceptional teammates and collaborating directly with industry leaders across software, AI, and infrastructure.

• Base salary range of $145,000–$200,000, depending on location, experience, and comparable internal compensation.

• Eligibility for meaningful equity through stock options in an early-stage, high-growth company.

• Eligibility to participate in company benefit plans, which may include health, dental, and vision coverage, a 401(k) with company match, flexible PTO, paid parental leave, commuter benefits, and relocation and visa support for eligible roles.