AI Software Engineer (Vehicle Engineering)

SpaceX•Hawthorne, CA

19h•$125,000 - $195,000

About The Position

SpaceX is seeking software engineers to develop AI systems that accelerate engineering and improve internal operations. The role involves developing, evaluating, and deploying AI capabilities for engineering and business needs, including agent tools and training environments, machine learning systems for limited-data settings, and internal AI applications. The work may evolve as capabilities and opportunities change. This position combines experimentation, software development, and secure infrastructure. The engineer will collaborate with engineers, domain experts, and business partners to take ideas from prototypes through deployment, using rigorous evaluations to assess performance, identify failures, and guide improvements. Candidates may have varied areas of expertise; strong fundamentals, careful experimentation, demonstrated contributions, and the ability to grow are valued. Prior aerospace experience is not required.

Requirements

Bachelor’s degree in computer science, data science, engineering, mathematics, or physics, or 4+ years of professional experience building and training AI/ML systems in lieu of a degree.
At least 1 year of Python programming, development, and software debugging on Linux.
At least 1 year of experience with an ML framework such as PyTorch, or practical experience developing agents and model-based applications.
At least 1 year of experience building or evaluating AI/ML systems through professional work, internships, research, or substantial independent projects.
Willingness to work extended hours and weekends as needed.
Must meet the stated U.S. export-control eligibility requirements or be eligible to obtain the required authorizations.

Nice To Haves

Understanding of evaluation fundamentals, including appropriate metrics, baselines, separation of training and evaluation data, error analysis, and reproducible experimentation.
Experience with Docker and familiarity with Kubernetes.
Familiarity with version control, software testing, and collaborative development practices.
Ability to explain technical decisions, work across disciplines, and learn unfamiliar tools and problem domains.
Experience designing task-specific evaluations, evaluation datasets, automated graders, or frameworks for comparing model and agent performance.
Experience building reinforcement learning environments, agent tools, reward functions, or verifiers.
Experience with model fine-tuning, reinforcement learning, or learning from human feedback.
Experience developing effective ML systems with limited data, including data curation, augmentation, and synthetic data generation.
Experience deploying and troubleshooting Kubernetes workloads and distributed job execution.
Experience building systems for experiment tracking and versioning datasets, models, environments, and evaluations.
Experience developing backend services, APIs, databases, and interfaces for reviewing AI outputs.
Experience implementing systems involving sensitive data, audit trails, or structured human oversight.
Experience taking a technical project from initial requirements through implementation, evaluation, and deployment.

Responsibilities

Build AI systems and applications that address engineering and business problems; work with users to understand requirements and define measurable success criteria.
Develop evaluations, benchmarks, and representative datasets to measure model and agent performance, compare approaches, and detect regressions.
Build automated graders and scoring methods, validate agreement with expert judgments, and investigate misleading results, data leakage, and other evaluation failures.
Create reproducible environments for training and evaluating agents, including task definitions, tool interfaces, constraints, and feedback signals.
Integrate models and agents with engineering tools, internal services, and company data; capture execution traces and results to support debugging and evaluation.
Establish baselines, run experiments, and analyze failures to assess whether changes to data, models, tools, or workflows produce meaningful improvements.
Develop data preparation, training, and evaluation workflows for problems with limited real-world data, including targeted data collection, labeling, augmentation, and synthetic data where appropriate.
Use Docker and Kubernetes to build and operate reliable workloads with appropriate isolation, access controls, resource management, logging, and reproducibility.
Collaborate with engineering, infrastructure, security, HR, and legal partners on requirements for data handling, traceable outputs, auditability, and human review.
Maintain deployed systems through testing, monitoring, ongoing evaluation, and operational debugging; improve quality, reliability, latency, and cost based on evidence and user feedback.

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 coverage.
Vision coverage.
Dental coverage.
401(k) retirement plan.
Short- and long-term disability insurance.
Life insurance.
Paid parental leave.
Various discounts and perks.
May accrue 3 weeks of paid vacation.
10 or more paid holidays per year.
Paid sick leave pursuant to Company policy.