Sr. Software Engineer, Full Stack (Data & Video Analysis)

SpaceX•Hawthorne, CA

1d•$165,000 - $265,000

About The Position

The Vehicle Analysis Tooling team builds applications used across production, test, and flight. This role owns the full path from platforms that ingest and process high-volume engineering data—including telemetry, sensor data, and video—to the tools and interfaces engineers use to review analysis and act on it, as well as the infrastructure those applications run on. The role spans multiple specialties; depth in data, video, or applied models is a starting point, with learning and shipping across the other areas part of the job. The engineer works directly with vehicle, propulsion, test, manufacturing, and inspection engineers to solve important analysis problems. The position leads technical direction, mentors engineers, moves prototypes into production, and operates reliable production applications and data systems. Compensation for Level 3 is $165,000–$265,000, with actual level and base salary determined based on job-related knowledge and skills, education, and experience. The role may also be eligible for long-term incentives, discretionary bonuses, and participation in the Employee Stock Purchase Plan.

Requirements

Bachelor's degree in computer science, engineering, mathematics, physics, or another scientific discipline, or 8+ years of professional software experience in lieu of a degree.
5+ years of software development experience.
3+ years of experience building and operating production applications or data systems, including full-stack development.
3+ years of experience building production Python systems, including data processing, services, or user-facing application code.
At least 1 year leading technical work, including design reviews, project direction, and responsibility for the quality of code the team ships.

Nice To Haves

Production experience with video platforms and infrastructure, including ingest, storage, transcoding, playback, and review of high-volume engineering video.
Strong familiarity with large language model tools and capabilities, including how they work and where they can be used, or production experience applying them to computer vision problems.
Experience shipping applied machine learning, statistical models, or computer vision into systems others depend on, or serving models for inference, including live inference; relevant tools include PyTorch, OpenCV, Ultralytics or other YOLO-family detectors, and scikit-learn.
Production experience with the Python data stack (Pandas, NumPy, SciPy) and PostgreSQL.
Experience with time-series or other signal data, including cleaning, transformation, feature extraction, and analysis, or with taking models from training through deployment and maintenance.
Experience profiling and optimizing production data systems or interactive applications, including query latency, memory use, throughput, or interactive rendering of large datasets, and maintaining improvements as volume grows.
Experience operating production infrastructure, including Linux, Docker, Kubernetes, HPC systems, a web application framework, CI/CD, batch or job processing, or observability.
Experience leading incident response and reliability work for multi-service applications, including monitoring and alerting.
Experience with a second language used for services or applications, such as Go, Rust, or C#.
Experience leading internal tools engineers rely on, including projects spanning hardware and software teams.

Responsibilities

Lead the design, implementation, and maintenance of full-stack applications and platforms used across production, test, and flight, including data processing that turns telemetry, sensor data, or video into analysis engineers can use and that feeds model inference.
Work directly with vehicle, propulsion, test, manufacturing, and inspection engineers on analysis problems, measuring success by time saved and problems caught.
Build internal tools and web interfaces engineers use to query, visualize, and act on engineering data and automated-analysis results.
Profile and improve performance in data processing, database queries, and interactive views of large telemetry or video; define how the team measures performance as volume grows.
Take statistical models, computer vision, or large language models from evaluation into production review workflows for anomaly detection and inspection, and keep result quality visible in production.
Move prototypes into production using automated tests, CI/CD, and a repeatable release process.
Set the team's architecture and practices for testing, CI/CD, observability, and code review.
Own application infrastructure, including deployments, monitoring, operations, capacity planning, and reliability.
Operate applications in production, respond to failures, restore service, and incorporate fixes into software and operating practices; improve monitoring and incident response as usage grows.
Work across processing, interfaces, models, and operations, learn unfamiliar areas quickly, and help other engineers do the same.
Mentor engineers and set technical direction for software and its operation.

Benefits

Potential eligibility for long-term incentives in the form of company stock or long-term cash awards.
Potential discretionary bonuses.
Ability to purchase additional stock at a discount through an Employee Stock Purchase Plan.
Comprehensive medical, vision, and dental coverage.
401(k) retirement plan.
Short- and long-term disability insurance.
Life insurance.
Paid parental leave.
Various other discounts and perks.
Accrual of 3 weeks of paid vacation.
10 or more paid holidays per year.
Paid sick leave pursuant to Company policy.