Head of Weather Systems

You will lead Zipline's Weather Intelligence team, defining and enforcing the environmental limits under which our aircraft operate safely and reliably. This owner-level role sits at the intersection of meteorology, software, flight operations, and safety. Your work makes automated go/no-go decisions for thousands of missions every day, directly affecting uptime, delivery throughput, customer experience, and regulatory safety posture. You will operate inside a high-velocity global logistics and aviation environment where decisions must be defensible to operators, regulators, and partners.

Responsibilities

• Own end-to-end development and operational performance of the automated weather intelligence and risk system used for mission go/no-go decisions across Zipline’s operational regions. You are the single technical and product owner for system behavior and reliability.
• Translate meteorological uncertainty into concrete, testable operational limits and encode those into automated decision logic used in daily operations.
• Define and measure success: set quantitative targets (e.g., uptime improvement, false downtime rate, missed-risk rate, delivery throughput impact) and deliver within the first 12 months measurable improvements compared with our first year of commercial operations.
• Lead validation efforts using a combination of simulation, historical log replay, flight test, and live operations; design experiments and acceptance criteria for new models, sensors, and rules before production rollout.
• Prioritize trade-offs across safety margin, operational availability, sensing cost, model complexity, and deployment speed; document failure modes and rollback criteria for all changes.
• Own cross-functional execution with Flight Operations, Safety, Flight Sciences, Simulation, Field Sites, and Engineering: define SLAs, incident roles, and escalation paths for weather-driven incidents.
• Drive the roadmap for sensing (ground and air), data ingestion, model tooling, and runtime decision services; sponsor necessary infrastructure (streaming, simulation, monitoring) to help meet operational SLAs.
• Hire, mentor, and manage a small technical team (3–5 engineers/scientists) or partner with an existing engineering manager while retaining product and technical ownership of the weather system.
• Operate an on-call / incident rotation for weather-system outages; run post-incident reviews, own corrective action plans, and feedback into validation and deployment processes.

Requirements

• Minimum 6+ years industry experience shipping mission-critical decision systems; demonstrable ownership of automated operational decision logic in production at scale.
• Required technical skills: deep fluency in scientific Python (Pandas, NumPy, Xarray, NetCDF), experience with time-series/streaming systems (Kafka or equivalent), and comfort reading/working with production stacks (Kubernetes, AWS, Rust/C++ codebases). Ability to prototype and ship models into production pipelines.
• Domain expertise: strong working knowledge of meteorology, aviation weather, or related geospatial environmental science; ability to explain physics, uncertainty, and operational implications to non-experts and regulators.
• Systems judgment: experience defining quantitative metrics (false-positive/negative tradeoffs), designing validation experiments (simulation + field tests), and documenting failure modes and rollback criteria.
• Operations intensity: history working with 24/7 operational systems, incident response, and SLAs; comfortable making and owning high-consequence trade-offs under uncertainty.
• Leadership: experience leading or directly influencing small cross-disciplinary teams and driving measurable improvements in uptime, throughput, or reliability within 6–12 months.
• Logistics & constraints: must be able to work onsite in South San Francisco full-time and travel internationally
• Must-have traits: decisive under uncertainty, rigorous about validation and failure modes, able to translate scientific complexity into deterministic operational rules and measurable outcomes.