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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:
SpaceXSI is building at a furious pace with the latest compute and switching hardware to help people understand the universe. We are looking for exceptional ML Infrastructure Engineers with deep expertise in high-speed interconnect technologies to design, build, and optimize the network fabric that powers large-scale AI training and inference clusters. This strategic role will drive innovation in high-bandwidth, low-latency, power-efficient interconnects critical for AI/ML clusters based on advanced computing platforms.
You will have the opportunity to work on all modalities of interconnects connecting GPUs and switches both inside and between data centers, including our primary front and backend networks that train Grok and that customers use for inference. Engineers will own all aspects from design and development to build and operations. You will be expected to define and improve team processes and to contribute to scaling and maintenance efforts.
You will focus on the physical layer and system-level integration of copper (ACC, AEC, CPC) and optical (FRO, LRO/TRO, LPO, AOC, CPO) interconnects that directly determine the performance, power efficiency, scale, and cost of next-generation AI/ML clusters. This is a highly technical, hands-on role bridging ML cluster requirements with cutting-edge interconnect hardware — ideal for engineers who love both large-scale AI systems and the physics/engineering of 200G+ SerDes, PAM4, photonics, signal integrity and diagnostics.
RESPONSIBILITIES:
- Design, validate, and productize high-speed copper and optical connectivity solutions for AI clusters (100k+ GPU scale).
- Own vendor due diligence and onboarding for new 1.6T products including AEC and pluggable optical transceivers (DR4/8, FR4) including rigorous bring-up & characterization.
- Investigate the opportunity for LPO and LRO in our network.
- Evaluate early co-packaged and near-packaged engines for switches and GPUs.
- Pathfinding for new interconnect modalities including VCSEL, microLED, THz radio-based solutions to improve network economics and reliability.
- Work closely with vendors (transceiver, cable, SerDes, DSP, silicon photonics foundries) to influence roadmaps and ensure timely delivery of next-gen solutions.
- Collaborate with ML training teams to translate workload communication patterns into concrete interconnect topology and optical reconfigurability requirements.
- Perform system-level simulation of end-to-end fabric performance.
- Drive failure analysis, root cause, and corrective actions for interconnect-related issues in production clusters through fleet-level metrics gathering and analysis.
- Contribute to internal tooling and automation for interconnect health monitoring, telemetry, diagnostics, remediation and automated qualification pipelines.
- Stay current with industry standards (OIF CMIS, IEEE) and emerging technologies (multi-core/hollow-core fiber, 448G SerDes, TFLN, ring resonators)
BASIC QUALIFICATIONS:
- At least 8+ years of hands-on experience in designing, deploying and operating high-speed copper and optical interconnects, preferably in a module design role or in a hyperscale datacenter environment.
- Master's or PhD degree in Electrical Engineering, Photonics or Physics.
- Deep knowledge of PAM4 SerDes performance, equalization, jitter, crosstalk.
- Solid operational understanding of FEC, Retimers, TIAs and Drivers.
- Deep knowledge of optical link budget analysis and performance metrics including TDECQ, OMA, Tcode, stressed receiver sensitivity and associated diagnostics.
- Expertise in transceiver components including CW lasers, SiPh PICs, EML, DSP, passive subassemblies, their failure modes and characterization.
- Knowledge of thermal, mechanical, power, signal integrity constraints in dense hardware.
- Knowledge of SiPh design process, yield improvement and reliability testing.
- Familiarity with CPO technologies and challenges/risk areas.
- Familiarity with subcomponent supply chains and global manufacturers, ODMs and CMs.
- Strong problem-solving skills and ability to thrive in a fast-paced, ambiguous setting.
COMPENSATION AND BENEFITS:
$180,000 - $440,000 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.
SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice*.*
Salary Context
This $180K-$440K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($310K) sits 46% above the category median. Disclosed range: $180K to $440K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SpaceXSI AI Hiring
SpaceXSI has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Palo Alto, CA, US. Compensation range: $156K - $440K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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