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About This Role
AST SpaceMobile is building the first and only global cellular broadband network in space to operate directly with standard, unmodified mobile devices based on our extensive IP and patent portfolio and designed for both commercial and government applications. Our engineers and space scientists are on a mission to eliminate the connectivity gaps faced by today's five billion mobile subscribers and finally bring broadband to the billions who remain unconnected.
Position Overview
We are seeking a Principal Automation and AI Operations Engineer to support the definition, lifecycle management, and documentation of the NAS and NCC SKUs within the Gateway and their integration with other Gateway elements — including the Q/V systems, eNB/gNB, BPMS, and Networking — as well as other OSS systems.
This role is responsible for designing automated test frameworks and scripts for Gateway subsystems, driving pre\- and post\-deployment validation of each Gateway SKU against golden configuration and performance baselines, and partnering with RF, hardware, software, and service delivery teams on test plans, pass/fail criteria, field trials, and FOAs to ensure Gateway operability under a highly autonomous operating model.
The ideal candidate is collaborative, methodical, and automation\-minded, and thrives in a fast\-paced environment where test coverage, manual\-touch reduction, and automation scope expand with each new Gateway SKU and feature.
Key Responsibilities:
- Define, lifecycle\-manage, and document the NAS and NCC SKUs within the Gateway.
- Design and build automated test frameworks and scripts for Gateway subsystems, including RF chain, baseband, BPMS, eNB/gNB, networking, and NCC/OSS interfaces.
- Drive pre\- and post\-deployment validation on each Gateway SKU against golden configuration and performance baselines.
- Partner with RF, hardware, and software engineering teams to align test plans and pass/fail criteria, and support field trials and FOAs.
- Work with Operations and OSS teams to enable end\-to\-end visibility and management of the Gateway.
- Automate repetitive operating and engineering tasks, and build event\-driven remediation workflows.
- Integrate telemetry with operational decision engines, and develop predictive failure and capacity models.
- Create automated incident triage and build engineering assistants for troubleshooting.
- Establish guardrails for AI\-driven operational actions and develop digital representations of network and service state (digital twins).
- Automate configuration validation prior to deployment, and measure automation coverage and manual\-touch reduction.
Qualifications
Education:
Bachelor's degree in computer science, computer engineering, electrical engineering, or a related technical field.
Experience:
- 5\+ years of experience in network automation, DevOps/SRE, or operations engineering, ideally within telecom, networking, or satellite ground\-segment environments.
- Building automated test frameworks that replace manual validation procedures at scale.
- Defining and lifecycle\-managing hardware/software SKUs and associated acceptance criteria.
- Supporting field trials, FOAs, or first\-deployment validation for new network products.
- Proven, hands\-on competence across network automation; Python, Go, or similar languages; infrastructure as code; event\-driven systems; workflow automation; data engineering; machine learning; AIOps; autonomous remediation; digital twins; and policy\-driven operations.
- Integrating telemetry pipelines with operational decision engines or automated remediation systems.
Preferred Qualifications:
- Experience building or operating toward a highly autonomous operations model.
- Familiarity with NAS and NCC systems, or equivalent core network/operations\-support platforms.
- Experience with predictive/ML\-based failure detection or capacity planning models in a production network.
- Familiarity with satellite, RF, or baseband subsystems and their test/validation requirements.
- Experience developing digital twins or simulation models of network/service state.
- Background establishing governance or guardrails for AI\-driven or autonomous operational actions.
Soft Skills:
- Strong interpersonal skills with the ability to build productive relationships across technical and non\-technical teams.
- Proven ability to collaborate effectively within cross\-functional engineering, product, and operations organizations.
- Excellent written and verbal communication skills, with the ability to clearly communicate complex technical concepts.
- Strong analytical and problem\-solving skills with a methodical approach to troubleshooting.
- Meticulous attention to detail to ensure the accuracy of technical documentation, test results, and project deliverables.
- Strong organizational and time\-management skills with the ability to manage multiple projects and priorities simultaneously.
- Ability to work independently while contributing effectively within a team environment.
Technology Stack:
- Python, Go, or similar languages for automation and tooling development.
- Infrastructure\-as\-code frameworks (e.g., Terraform, Ansible, or equivalent).
- Event\-driven and workflow\-automation platforms.
- Data engineering and telemetry pipelines (e.g., streaming/message\-bus systems, time\-series databases).
- Machine learning and AIOps platforms for predictive analytics and automated remediation.
- Digital twin/simulation tooling for network and service state modeling.
- CI/CD and automated test frameworks for RF, baseband, BPMS, eNB/gNB, and networking subsystems.
- NCC/OSS platforms and their APIs for end\-to\-end Gateway visibility and management.
Physical Requirements
- Ability to work in a standard office environment and use a computer for extended periods.
- Ability to work in laboratory and technical testing environments.
- Ability to travel periodically to support deployment, integration, testing, and operational activities.
- Ability to safely handle and move technical equipment and hardware components when necessary.
- Ability to capture, analyze, and document technical test procedures, results, and system performance data.
*This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands**.*
AST SpaceMobile is an Equal Opportunity, at will Employer; employment 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.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At AST SpaceMobile, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AST SpaceMobile AI Hiring
AST SpaceMobile has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Lanham, MD, US, Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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