AI Software Engineer (Vehicle Engineering)

SpaceX•Hawthorne, CA

About The Position

SpaceX is seeking an AI Software Engineer to build, evaluate, and deploy AI capabilities that accelerate engineering and improve internal operations. The role 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 evaluation to measure performance, identify failures, and guide improvements. Work may include building tools and training environments for agents, developing machine learning systems with limited real-world data, and delivering internal AI applications. The work will evolve as new capabilities and opportunities emerge. Candidates may have depth in different areas; SpaceX values strong fundamentals, careful experimentation, demonstrated contributions, and the ability to grow across the work described. Prior aerospace experience is not required.

Requirements

Bachelor’s degree in computer science, data science, engineering, math, or physics; or 4+ years of professional experience building and training AI/ML systems in lieu of a degree.
1+ years of Python programming and developing and debugging software on Linux.
1+ years of experience with an ML framework such as PyTorch, or practical experience developing agents and model-based applications.
1+ years 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.

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 with people 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 for engineering and business problems, working 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, capturing execution traces and results to support debugging and evaluation.
Establish baselines, run experiments, and analyze failures to determine 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 to implement requirements for data handling, traceable outputs, auditability, and human review.
Maintain deployed systems through testing, monitoring, ongoing evaluation, and operational debugging, improving 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.
Access to a 401(k) retirement plan.
Short- and long-term disability insurance.
Life insurance.
Paid parental leave.
Various other discounts and perks.
May accrue 3 weeks of paid vacation.
Eligible for 10 or more paid holidays per year.
Paid sick leave pursuant to Company policy, which satisfies or exceeds applicable accrual, carryover, and use requirements.