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Selected role

Full-Stack Data Scientist, Hardware Reliability (Starlink)

SpaceX Bastrop, TX, US

Senior Full-time

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.

FULL-STACK DATA SCIENTIST, HARDWARE RELIABILITY (STARLINK)

SpaceX is leveraging our experience building rockets and spacecraft to deploy Starlink, the world's largest satellite constellation and most advanced broadband internet system. We provide reliable and fast internet to millions of users worldwide, including populations with little or no connectivity, rural communities, aircraft, watercraft, and places where existing services are unreliable, too expensive, or disconnected by natural disasters. We design, build, test, and operate all parts of the system, thousands of satellites and millions of consumer antennae that allow users to connect within minutes of unboxing. The Starlink team is seeking out the best-in-class professionals to maximize Starlink's potential for communities and businesses around the globe.

As a Full-stack Data Scientist on the Starlink Reliability team, you will build the data pipelines, models, and production scoring systems that improve the reliability of Starlink customer hardware (dishes, routers, power supplies, cables). You will work from telemetry, manufacturing, inventory, field-return, and unstructured support data (tickets, chats, images) through production pipelines and deployed models, put scores into the workflows that drive replacements and support decisions, and build LLM-backed tools — retrieval, tool calling, and agentic workflows — that engineers and operators run. You will measure whether those systems reduce failures as we launch new products and scale the network. The team is based in Bastrop, on site with the factory that builds this hardware.

RESPONSIBILITIES:

Build and maintain data pipelines, infrastructure, and software to objectively increase the reliability of Starlink customer hardware
Design and operate the feature, label, and scoring data that reliability models train and serve on, including backfills, late-arriving data, and data quality
Train, validate, and iterate models for hardware failure prediction both in production line and field
Deploy and operate models in production, including scheduled scoring, versioning, monitoring, and iteration based on live results
Contribute to critical Starlink software repositories, working in core internal and customer-facing codebases
Build internal analytics tools, APIs, and applications used by hardware, production, quality, supply chain, and customer experience teams
Collaborate across engineering, production, test, inventory, quality, supply chain, and customer experience to turn analyses and predictions into design, process, and support changes
Identify customer needs, gather requirements, and execute independently in a dynamic environment with changing constraints and tight deadlines

BASIC QUALIFICATIONS:

Bachelor's degree in computer science, data science, mathematics, electrical engineering, or other STEM discipline
4+ years of professional experience in data science, machine learning, or software engineering
Strong proficiency in Python and SQL
Experience building production data pipelines
Experience deploying machine learning models in production

PREFERRED SKILLS AND EXPERIENCE:

Master's degree in computer science, data science, mathematics, or other STEM discipline (or PhD with 3+ years of relevant experience)
Strong understanding of software engineering fundamentals including data structures, algorithms, system design, concurrency, and writing high-performance, maintainable code
Experience with relational databases, data pipeline orchestration, and large-scale data processing
Experience deploying, versioning, and monitoring machine learning models in production, including scheduled scoring jobs whose outputs are consumed by other systems
Experience building APIs or internal tools that deliver model outputs to engineering users
Experience shipping large language models in production, including retrieval-augmented generation (RAG), tool calling, and agentic workflows
Experience extracting signal from unstructured data (support tickets, chat, call transcripts, images) alongside structured telemetry and field-return data
Strong analytical skills: statistical analysis, data visualization, quantitative reasoning, and reliability methods (survival analysis, calibration, experimental design)
Familiar with at least one additional programming language (e.g. Go, Java, C++, C#)
Excellent written and verbal communication skills; ability to make presentations to engineers, team members, internal customers, and management

ADDITIONAL REQUIREMENTS:

This position is based in Bastrop, TX (Austin area) and requires being on-site; hybrid or remote work not considered
Must be willing to work extended hours and weekends as needed
Must be willing to travel up to 10%

ITAR REQUIREMENTS:

To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.

SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

Applicants wishing to view a copy of SpaceX's Affirmative Action Plan for veterans and individuals with disabilities, or applicants requiring reasonable accommodation to the application/interview process should reach out to [email protected].

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