Senior Autonomy Data Infrastructure and Analytics Software Engineer

Joby Aviation•Santa Cruz, CA

About The Position

Joby Flight Research designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations. Joby Flight Research is seeking a Staff Software Engineer, Autonomy Data Infrastructure and Analytics to build the data infrastructure and evaluation platform for our Superpilot™ autonomy stack. This position offers a unique opportunity to build the data infrastructure that powers Joby’s varied fleet of autonomous aircraft. In this role, you will own the end-to-end data lifecycle, from the moment a flight log leaves our aircraft to the moment it generates safety-critical metrics. You will design robust infrastructure for flight telemetry, sensor, and imagery data, turning raw logs into reliable datasets, metrics, and workflows that support development, validation, and operational insight. Your work will help teams detect regressions, investigate edge cases, and validate safety-critical behavior long before it reaches flight operations. We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with perception, controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong software engineer who cares deeply about system design, reliability, developer experience, and enabling fast, safe iteration across multiple aircraft programs.

Requirements

5+ years of experience architecting and operating large-scale distributed systems and data infrastructure
Expertise in developing databases, query engines, and storage backends for high-frequency, high-cardinality time series data
Experience with one of the “Big Three” cloud providers (AWS, GCP, Azure) and Infrastructure as Code (IaC) tools like Terraform or Kubernetes
Deep understanding of backend, streaming, and batch processing architectures
Strong proficiency in Python, C++, and Git within a Linux-based environment
Experience processing high-bandwidth sensor data from robotics or autonomous platforms (e.g., GPS, IMU, Lidar, Radar)
Proven ability to document complex technical designs, architectural trade-offs, and implementation roadmaps
Champion of software best practices, including rigorous code reviews and mentorship.
Excellent communication skills for collaborating with cross-functional teams
This position must meet US export control compliance requirements, therefore a candidate must qualify as a “US Person” as defined by 22 C.F.R. § 120.15. “US Person” includes US Citizens, lawful permanent residents, refugees, or asylees.

Nice To Haves

Expertise in designing distributed batch processing and workflow orchestration systems for large-scale evaluation (e.g. Airflow, Spark, Ray, or Temporal)
Familiarity with Databricks
Building dataset management infrastructure
Experience with autonomous vehicles
Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.)
Experience with hardware-in-the-loop (HIL) workflows
Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code
Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment)
Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow)

Responsibilities

Design, implement, and maintain a highly-scalable ingestion pipeline for a heterogeneous fleet of aircraft, owning the data schemas, APIs, and associated tooling.
Transform raw flight data into queryable, well-structured databases to unlock organization-wide insights.
Develop data augmentation scripts to help catalog the data (metadata, anomaly detection, missing data). This might include novel statistical sampling methods and machine learning solutions to maximize the value of collected data.
Establish data stewardship for cost control, access control, integrity, traceability, and regulatory compliance.
Build the analysis platform that transforms raw flight and simulation data into trustworthy metrics, curated datasets, and actionable engineering insight.
Develop scalable pipelines for flight physics analysis, system health monitoring, autonomy evaluation, and predictive maintenance.
Create reusable tooling for offline replay, scenario mining, metric computation, labeling, and regression analysis across large datasets.
Define and operationalize evaluation frameworks that allow teams to measure autonomy performance consistently across aircraft, environments, and software versions.
Drive investigation of anomalies and edge cases by connecting logs, sensor data, simulation outputs, and derived metrics.
Work cross-functionally with autonomy, perception, controls, and flight software teams to deliver analysis capabilities that are robust, self-serve, and tightly integrated with development workflows.
Develop the scheduling and execution engine for teams to run massive batch jobs on ingested data (e.g. post-processing, flight replay, metrics, “what-if” scenarios)
Provide the tools, APIs, and dashboards that empower autonomy teams to self-serve evaluation workloads, from recurring CI pipelines to punctual experiments.
Proactively explore the data, develop new metrics, interpret trends, and investigate anomalies in data from both real world and simulation.
Collaborate closely with the rest of the Superpilot™ team to ideate, plan and execute on high-quality, well-integrated solutions and features.
Monitor system health and performance, proactively addressing issues and providing user support.