DroneDeploy

Business Operations

• Own the GTM systems stack end-to-end, including Salesforce, Salesforce CPQ, Marketo, Clay, ZoomInfo, CommonRoom and the integrations between them.
• Lead Salesforce architecture and governance across objects, fields, flows, validation rules, routing logic, and integration endpoints.
• Build automation and AI-powered workflows that remove manual work across lead routing, lifecycle management, handoffs, enrichment, and other core GTM motions.
• Own data quality and enrichment infrastructure across Salesforce and Marketo to support ABM, outbound, forecasting, and operational visibility.
• Turn ambiguous requests from Sales, Marketing, and Customer Success into scalable system solutions with clear tradeoffs, strong follow-through, and durable documentation.
• Manage and develop a small team, set priorities, unblock work, and create the operating rhythm that keeps GTM systems fast, reliable, and maintainable.

Senior Manager, Business Systems

Data Engineer

• Build and maintain robust dataset and evaluation infrastructure, including ground-truth quality controls
• Diagnose and fix measurement-path issues between offline evals and production accuracy
• Iterate on and scale existing labeling frameworks and knowledge capture systems
• Own prompt optimization across its full lifecycle, from candidate selection through production rollout and post-launch validation
• Automate eval pipelines and build the tooling to run this at scale across multiple AI products
• Create reusable internal tooling for error analysis, audits, and experimentation, and prioritize work using likely impact

Senior Software Engineer, Data Science – AI Accuracy

Research Engineer

• Design, train, evaluate, and ship 3D reconstruction systems across our pipeline — gaussian splats, foundation 3D models, SfM, MVS, monocular depth, mesh reconstruction.
• Drive integration of modern reality capture approaches (splatting, foundation models) into our production stack — making the calls on what's ready to ship and what isn't.
• Own the hardest technical investigations in your area, from initial triage through production rollout and long-tail support.
• Optimize 3D systems for speed, accuracy, and efficiency at production scale.
• Use the right tool for the problem — classical 3D computer vision when it wins, learned approaches when they win.
• Stay current with 3D vision research and evaluate promising techniques against our workflows.
• Hold a high technical bar for your own work — high-quality designs, well-tested code, production-ready ship habits.
• Contribute to the team through code review, pairing, and design feedback.
• Codify debugging and investigation playbooks into reusable skills.
• Use AI tools daily across the SDLC, with judgment on where they help and where they don't.
• Author agent skills or tooling that other engineers use; contribute to the team's shared skills library.
• Conduct rigorous evaluations of new AI tools and bring useful patterns to the team.
• Review agent-generated code with the same rigor as human-written code.
• Track the AI tooling landscape and bring useful patterns to the team.

Senior Applied Research Engineer, 3D Computer Vision

Solutions Engineer

• Partner with prospective and existing customers to understand business goals, operational workflows, technical constraints, and success criteria.
• Architect scalable solutions that align customer needs with DroneDeploy product capabilities, implementation requirements, and long-term value.
• Lead technical discovery, solution scoping, demonstrations, proof of concepts, and validation activities for complex customer opportunities.
• Support deployments and implementations, including configuration, integration planning, training, troubleshooting, and optimization.
• Act as the primary technical advisor in customer-facing engagements, clearly communicating risks, tradeoffs, recommendations, and next steps to technical and non-technical stakeholders.
• Guide customers through hardware, connectivity, and interoperability considerations across robotics, docks, sensors, infrastructure, and software systems.
• Partner with Product and Engineering to define, test, and improve emerging workflows across robotics, walkthrough AI, automation, and related reality capture use cases.
• Bring structured field feedback into roadmap and product improvement discussions to improve adoption, reliability, and solution quality.
• Develop playbooks, documentation, demos, and enablement assets that improve repeatability and scale across the Solutions team.
• Support technical responses for proposals, RFPs, business cases, and executive-facing customer presentations.
• Identify opportunities to streamline workflows and reduce operational friction through tooling, automation, and process improvements.
• Mentor internal teammates on solution design, deployment practices, and field learnings.

Senior Solutions Architect

QA Engineer

• Field testing - Daily drone flights for regression, acceptance, and validation. You need physical space to fly (e.g. 10+ acres accessible from your location) and the judgment to run a high-quality flight program on your own.
• Test planning and execution - Define comprehensive test plans for new features and releases. Know the requirements better than the PM or EM. Catch edge cases before anyone else sees them.
• QA team coordination - Design and schedule QA test programs, write clear plans and checklists, and reduce back-and-forth so QA is never the bottleneck.
• Bug reporting quality - Own the bug reporting rubric (repro steps, severity, exposure, context, logs, media) and enforce it so triage is plug-and-play - no validation pass needed to understand a ticket.
• Regression strategy - Manage Dock, Android, and iOS regression suites. Own the "regression found" playbook. Make sure the suites actually test user workflows and catch what matters.
• Automated testing - Build and maintain automation to reduce manual testing load and increase confidence. AI-first approach to test setup, maintenance, and analysis.
• Quality metrics - Track post-release bugs per feature, escalation patterns, QA throughput, and SLO-violating issues. Use them to improve processes and focus QA resources where they reduce risk most.
• Equipment and environments - Maintain Qase suites, device/drone inventory, firmware tracking, and internal QA tooling. No fire drills because something wasn't set up.

Senior Quality Assurance Engineer

Full-stack Engineer

• Lead the design, evaluation, and deployment of our production photogrammetry pipeline — feature detection and matching, structure from motion, multi-view stereo, monocular depth estimation, mesh reconstruction, and the supporting infrastructure that makes them work at scale.
• Drive integration and evaluation of newer 3D reconstruction approaches — gaussian splats, foundation 3D models (MASt3R / DUSt3R-class and successors), and what comes next — where they earn their place in the pipeline.
• Optimize 3D systems for speed, accuracy, and robustness at production scale.
• Use the right tool for the problem — classical 3D computer vision when it wins, learned approaches when they win.
• Own the hardest technical investigations end-to-end — initial triage through production rollout and long-tail support.
• Set the technical bar for photogrammetry on the team — review designs, raise the quality of experiments, codify what "great" looks like.
• Own the production observability standard for photogrammetry — KPIs, instrumentation (metrics, logs, dashboards, alerts), and the processes that catch data-quality issues early and sustain performance at scale.
• Mentor engineers on the team, then take on 1–2 direct reports in your first year and grow your scope over time as the sub-team expands.
• Drive execution of the photogrammetry core roadmap in tight partnership with CV leadership. Translate strategic direction into shipped work, sequence it well, and call out when something isn't going to land in time to course-correct.
• Codify debugging and investigation playbooks into reusable skills to empower the team and support the organization, minimizing escalations.
• Reshape how the team uses AI across the SDLC — driving adoption of AI-first and prototype-first workflows and pushing the bar on what these tools should do for us.
• Author reusable tooling that scales the team — agent skills, prompts, debugging playbooks, custom tooling — so other engineers (and our support org) can stand on your shoulders rather than escalate.
• Run rigorous evaluations of AI tools — where they help, where they hurt, and how to tell.
• Drive the adoption of prototype-first and AI-first development workflows.
• Review agent-generated code with the same rigor as human-written code, and establish standards for trust and reliability.
• Treat the AI tooling landscape as a moving target: actively track what's new, run it against current problems, and champion effective new tools across the team.

Lead Software Engineer, 3D Computer Vision

• Drive the technical vision and architecture for Aerial Capture, driving clean code standards, setting technical direction, and making key architectural decisions.
• Serve as a primary code contributor, lead projects end-to-end, and proactively coordinate work to ensure predictable delivery, technical quality, and the quick unblocking of teammates.
• Raise the technical bar across the team by providing hands-on mentorship, pairing, and timely, direct technical feedback to foster engineer growth.
• Take ownership of system health, including reliability and maintainability. Champion the reduction of technical debt and measure outcomes to drive value throughput.
• Act as the technical voice for the team in cross-functional forums, communicating decisions, trade-offs, and project status clearly to Engineering Managers and stakeholders.