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About This Role
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 making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
ABOUT THE ROLE:
We are seeking a mid-level Governance, Risk, and Compliance (GRC) Engineer to join our security and AI governance team at SpaceXSI. You will set the standards the organization builds to, design and implement controls, and automate the unglamorous parts of compliance so a fast-moving team can ship safely. The ideal candidate combines deep fluency across modern security and AI frameworks with GRC engineering skills: you translate control requirements into technical implementations, partner with engineers to bake compliance into architecture and CI/CD, and replace point-in-time checklist work with continuous, engineered assurance. You will collaborate across engineering, legal, product, and leadership to keep our AI systems audit-ready across enterprise, commercial, and public-sector environments.
This role may also include additional tasks and responsibilities as needed to support the team and evolving business priorities.
RESPONSIBILITIES:
- Execute security compliance implementation and audits across core frameworks including SOC 2, NIST CSF, NIST SP 800-53, ISO 27001, ISO 42001, and the EU AI Act, including control design, mapping, gap assessment, evidence collection, and remediation tracking.
- Build and maintain Compliance-as-Code and continuous compliance capabilities — policy-as-code, automated control validation, continuous evidence pipelines, and monitoring integrated into development and deployment workflows — so the company can move fast without cutting corners.
- Operate and extend GRC platforms (e.g., Vanta) as the system of record for controls, evidence, and audit readiness; integrate them with cloud, identity, and engineering tooling to reduce manual toil.
- Partner with engineering and architecture to embed compliance requirements early in design reviews; translate framework obligations into clear technical control narratives that satisfy auditors without slowing delivery.
- Develop, maintain, and continuously improve corporate policies, standards, and procedures that support the company's governance and AI management system posture.
- Identify, assess, and prioritize risks related to AI/ML operations, cybersecurity, regulatory compliance, data privacy, intellectual property, and cloud deployments; distinguish meaningful business risk from compliance theater.
- Lead risk assessments and compliance reviews for new products, model deployments, features, and architectural changes, with particular attention to AI system risks (data handling, model governance, agentic and conversational surfaces).
- Own and cultivate relationships with external auditors, assessors (e.g., QSAs where applicable), and regulators; serve as the bridge between auditors and internal teams so requests are reasonable, clear, and relevant to our stack.
- Champion a culture of security and compliance across the company — educating teams on why controls exist, not only enforcing them.
BASIC QUALIFICATIONS:
- Bachelor's degree in computer science, Information Security, Cybersecurity, or in an engineering/STEM field.
- 3+ years of experience in GRC, security compliance, or technology audit roles with hands-on GRC engineering responsibilities.
- Demonstrated experience implementing and maintaining security compliance frameworks in cloud environments (AWS, GCP, or Azure).
- Expert-level working knowledge of several of the following: SOC 2, NIST CSF, NIST SP 800-53, ISO 27001, and ISO 42001 — including building and running controls, not only reading the frameworks.
- Experience with Compliance-as-Code practices and GRC automation tooling (e.g., Vanta, Drata, or similar), with a bias toward continuous monitoring.
- Ability to evaluate control objectives against real IT and cloud configurations and to work alongside engineers on remediation.
PREFERRED SKILLS AND EXPERIENCE:
- 5+ years of security compliance, GRC engineering, or technology audit-related experience.
- Hands-on experience implementing technical controls (e.g., IAM, logging and monitoring, encryption, infrastructure hardening) and integrating compliance checks into CI/CD pipelines.
- Experience in the tech or AI/ML industry, particularly with startups or high-growth product organizations.
- Working knowledge of HIPAA privacy and security rules (bonus), ideally mapped into a SOC 2 or ISO-certified control environment.
- Experience supporting SOX / ITGC design, documentation, testing, or auditor coordination, especially in a publicly traded or IPO-bound company.
- Strong understanding of AI ethics and AI governance frameworks (e.g., NIST AI RMF, ISO 42001, EU AI Act) and associated operational risks.
- Working knowledge in data privacy frameworks (e.g., GDPR, CCPA) in a technology or cloud environment.
- Exceptional analytical, problem-solving, organizational, and project management skills, with the ability to take compliance programs from conception to audit-ready launch.
- Excellent communication and stakeholder management skills — able to explain risk and tradeoffs to engineers, legal, sales, and executives in plain language.
- Certifications such as CISSP, CISA, CISM, CRISC, CGEIT, ISO 27001 Lead Implementer/Auditor, or similar preferred.
- Experience with public sector or federal compliance programs (e.g., FedRAMP, NIST 800-171, CMMC) is a plus.
COMPENSATION AND BENEFITS:
$100,000 - $198,000 USD
Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.
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.
SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice*.*
Salary Context
This $100K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SpaceXSI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($149K) sits 30% below the category median. Disclosed range: $100K to $198K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
SpaceXSI AI Hiring
SpaceXSI has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, Palo Alto, CA, US, Remote, US. Compensation range: $156K - $440K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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