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About This Role
Voyager is an innovative defense, national security and space technology company committed to advancing and delivering transformative, mission\-critical solutions. We tackle the most complex challenges to unlock new frontiers for human progress, fortify national security, and protect critical assets to lead in the race for technological and operational superiority from ground to space.
Forge the Future: Join Voyager Technologies
The future belongs to those who build it. At Voyager Technologies, we're building technologies that protect lives, expand frontiers and prepare us for what's next. And we're doing that with people who are wired to solve, build, adapt and lead. These roles are not for the faint of heart.
You'll help lay the foundation for humanity's future. Join a culture where innovation thrives, curiosity is rewarded, and impact is real. We're a company of doers, thinkers and builders, united by purpose and grounded in reality.
If you want to put your skills to work where the stakes are real and the mission is bigger than any one person, forge the future with Voyager.
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Job Summary:
We are seeking a Principal Technical Director, AI\-Enabled Spectrum Dominance to serve as the senior technical leader for our advanced RF systems, software, algorithms, and agentic AI portfolio as applied to space\-based and terrestrial electromagnetic warfare and sensing. In this pivotal role, you will drive the technical vision for the future of spectrum dominance while simultaneously leading the application of Voyager's Agentic Computational Engineering (ACE) platform to RF hardware systems and digital signal processing (DSP) development.
This role uniquely combines deep RF domain expertise with leadership over AI\-driven hardware design and simulation workflows. You will oversee how agentic AI systems are deployed to compress RF hardware development cycles—from antenna design and transceiver architecture to DSP algorithm development and system\-level integration—accelerating the path from concept to deployable, mission\-critical capability for the warfighter.
The right person has deep familiarity with hardware, software, and algorithms across a variety of RF functions, including communications, electronic warfare (EW), radar, and sensing, with particular emphasis on space\-based active and passive RF systems. You must be as comfortable defining and executing complex research and development programs as you are productizing outcomes into monetizable commercial motions. You must also have the vision and technical fluency to direct how multi\-agent AI platforms transform traditional RF engineering workflows.
You will be joining the Advanced Technology Development team, a specialized unit within our Strategic Growth and Technology Organization. This team is responsible for defining and developing Voyager's future technologies, with a relentless focus on high\-impact, high\-ROI technology and product R\&D. You will work at the intersection of Voyager's Spectrum Dominance portfolio and the Agentic Computational Engineering (ACE) team—the group building Voyager's Generative Engine, the AI\-native platform that compresses complex hardware development cycles from years to days.
You will be joining the Advanced Technology Development team, a specialized unit within our Strategic Growth and Technology Organization. This team is responsible for defining and developing Voyager's future technologies, with a relentless focus on high\-impact, high\-ROI technology and product research and development. Our technology portfolio spans the totality of Voyager's capabilities and beyond—we are tasked with creating both leap\-ahead capabilities in existing technology areas and strategically moving into and developing foundational technology in new, uncharted areas.
Responsibilities:
- ### Technical Leadership \& Vision
+ Define and drive the technical roadmap for Voyager's Spectrum Dominance portfolio, serving as the ultimate technical authority for RF, EW, radar, and sensing programs—with specific emphasis on space\-based active and passive RF systems.
+ Set the strategic direction for applying Agentic Computational Engineering to RF hardware design, DSP algorithm development, and end\-to\-end RF system integration.
+ Own the vision for how AI agents are used across the RF hardware design lifecycle, from generative antenna and front\-end design through automated DSP algorithm synthesis and simulation\-in\-the\-loop optimization.### Agentic Computational Engineering for RF \& DSP
+ Lead and oversee the deployment of Voyager's ACE platform (the Generative Engine) for RF\-specific applications, ensuring AI agents can autonomously design, simulate, evaluate, and optimize RF hardware and DSP subsystems.
+ Direct the development of domain\-specific AI agents for RF engineering workflows—including agents for electromagnetic simulation, transceiver architecture exploration, waveform design, interference analysis, and signal chain optimization.
+ Partner with the ACE platform team to define requirements for agent orchestration, physics simulation integration (EM solvers, thermal analysis, signal integrity), and design\-for\-manufacturing constraints specific to space\-qualified RF hardware.
+ Establish automated simulation loops between RF/DSP design agents and high\-fidelity physics solvers, including meshing, solving, and post\-processing for antenna patterns, RCS analysis, link budgets, and spectral performance.
+ Drive adoption of AI\-first workflows across the RF engineering organization, championing use of LLM\-based tools and agents to accelerate documentation, design review, test planning, and compliance verification.### R\&D Execution
+ Lead the execution of complex, high\-stakes research and development programs for next\-generation space\-based and terrestrial RF systems, ensuring technical risks are managed and innovative solutions are delivered on schedule.
+ Oversee the design, analysis, and testing of advanced RF systems including satellite communications payloads, space\-based SIGINT/ELINT collectors, synthetic aperture radar (SAR), and passive RF sensing architectures.### End\-to\-End System Architecture
+ Architect complete RF systems bridging DSP algorithms, software\-defined radio (SDR) hardware, FPGA/SoC platforms, and high\-level mission software, ensuring seamless integration across the entire signal chain for both space and ground segments.
+ Define system architectures for space\-based RF payloads that account for the unique constraints of the orbital environment: radiation tolerance, thermal management, power budgets, size/weight limitations, and long\-duration mission reliability.### Productization \& Commercialization
+ Bridge the "valley of death" by guiding technologies from TRL 3–6 research efforts into robust, productized solutions ready for manufacturing and deployment. Work closely with product and business teams to define commercialization strategies.
+ Leverage agentic AI capabilities to accelerate productization timelines, using the Generative Engine to rapidly iterate on design variants and manufacturing\-ready configurations.### Team Building \& Mentorship
+ Build and inspire a world\-class engineering team spanning RF hardware, DSP, and AI\-enabled design disciplines. Mentor senior engineers and researchers, fostering a culture of technical rigor, creativity, and rapid iteration.
+ Grow and lead a multidisciplinary team that includes RF engineers, DSP developers, and AI/ML engineers working collaboratively on agentic RF design capabilities.### Cross\-Functional Collaboration
+ Partner with business development and capture teams to shape new opportunities, providing technical grounding to win major government contracts.
+ Collaborate with the ACE platform team, propulsion, electronics, and manufacturing domain teams to ensure RF\-specific agentic capabilities integrate seamlessly with Voyager's broader Generative Engine ecosystem.
Required Qualifications:
- Bachelor's degree in Electrical Engineering, Physics, Mathematics, or a related field.
- Must be eligible to obtain and maintain a US Government clearance (requires US citizenship).
- Space\-Based RF Systems Expertise: Demonstrated background in the design, development, and deployment of space\-based active RF systems (such as satellite communications transponders, SAR payloads, space\-based radar, or active phased arrays for orbital platforms) and/or passive RF systems (such as space\-based SIGINT/ELINT collectors, radiometers, or passive sensing payloads). Must have direct experience with the unique constraints of the space environment, including radiation hardening, thermal management, power budgeting, and long\-duration reliability.
- Deep RF \& Systems Expertise: Proven background in designing and developing complete end\-to\-end RF systems (both RF front\-ends and digital subsystems). Deep understanding of the physics of the spectrum and the hardware required to dominate it across communications, EW, radar, and sensing domains.
- SDR Proficiency: Extensive experience designing and developing complex software\-defined radio (SDR) systems, including familiarity with modern transceiver architectures and FPGA/SoC platforms.
- Algorithm Development: Strong track record in designing and developing complex digital signal processing (DSP) and RF Machine Learning (RFML) algorithms. Understands how to apply modern compute—including AI/ML techniques—to solve traditional RF problems.
- R\&D Leadership: Demonstrated experience leading multi\-disciplinary engineering teams (Hardware, Software, RF, FPGA, AI/ML) through the full lifecycle of advanced R\&D programs, from white paper to prototype to deployment.
- Domain Expertise: Demonstrated background fielding capability in one or more of the relevant spectrum dominance domains: SIGINT, EW, or Radar, with preference for space\-based applications.
- Programmatic Execution: Familiarity with the unique challenges of government R\&D contracting, including experience delivering against technical milestones for customers like DARPA, ONR, AFRL, NRO, or the Space Force.
- Proposal Development: Proven proposal development experience, including contributing to technical volumes and cost estimations for complex government solicitations.
Preferred Qualifications:
- Master's or PhD in Electrical Engineering, Physics, Mathematics, or a related field.
- Active U.S. Top Secret / SCI Security Clearance.
- Direct experience with space\-based RF payload programs (design, integration, test, and on\-orbit operations) for DoD or Intelligence Community customers.
- Experience with or strong interest in AI\-driven engineering workflows, including agentic AI systems, LLM\-based design tools, automated simulation frameworks, or generative design platforms.
- Familiarity with agent orchestration frameworks (e.g., LangGraph, AutoGen, or custom agent runtimes) and an understanding of how multi\-agent systems can be applied to hardware design problems.
- Experience with electromagnetic simulation tools (HFSS, CST, FEKO, or equivalent) and a vision for how AI agents can automate and accelerate simulation\-driven RF design.
- Experience productizing advanced R\&D into monetizable commercial motions, demonstrating a track record of turning science projects into shipping products.
- Demonstrated history of leading winning proposals for space\-based RF programs.
- Background in orbit\-specific RF considerations: Doppler compensation, atmospheric propagation effects, space\-to\-ground link budgets, anti\-jam architectures, and low\-probability\-of\-intercept (LPI) waveforms.
- Willingness to travel up to 25% to customer sites, test ranges, and Voyager locations.
Please click "Apply" to submit your application.
Voyager offers a comprehensive, total compensation package, which includes competitive salary, a discretionary annual bonus plan, paid time off (PTO), a comprehensive health benefit package, retirement savings, wellness program, and various other benefits. When you join our team, you're not just an employee; you become part of a dynamic community dedicated to innovation and excellence.
Voyager is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
Minority/Female/Disabled/Veteran
*The statements contained in this job description are intended to describe the general content and requirements for performance of this job. It is not intended to be an exhaustive list of all job duties, responsibilities, and requirements. This job description is not an employment agreement or contract. Management has the exclusive right to alter the scope of work within the framework of this job description at any time without prior notice.*
Salary Context
This $225K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Voyager Technologies, Inc., 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($262K) sits 20% above the category median. Disclosed range: $225K to $300K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Voyager Technologies, Inc. AI Hiring
Voyager Technologies, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $300K - $300K.
Location Context
AI roles in Los Angeles pay a median of $215,000 across 397 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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