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About This Role
SNC can only hire in these states: AL, AK, AZ, CA, CO, FL, GA, HI, ID, IL, IN, KS, KY, LA, MD, MA, MI, MN, MS, MO, MT, NE, NV, NH, NJ, NM, NC, OH, OK, OR, PA, SC, TN, TX, UT, VA, WA, WV, WI.
In this role, you will help shape the long\-term AI/ML technical vision for the organization, guide high\-impact R\&D initiatives, and lead the development of advanced autonomy, perception, analytics, and generative AI capabilities. You will be responsible for setting technical direction across multiple simultaneous efforts, defining architectural standards, and ensuring that JTF Sierra’s prototypes and initiatives represent industry\-leading innovation. This role requires exceptional technical depth, the ability to operate with extreme autonomy, and the leadership presence to influence engineering culture, collaborate with program leadership, mentor staff, and represent the team to senior executives, customers, and external partners.
The ISR (Intelligence, Surveillance \& Reconnaissance), Aviation, and Security (IAS) business area is a leader in ISR and aviation, it is a leading prime manned and unmanned aircraft systems integrator for innovative, high\-performance ISR and aviation systems. Its end\-to\-end Command, Control, Computers, Communications and Intelligence, Surveillance \& Reconnaissance (C4ISR) capabilities encompass design, integration, test, certification, ground/flight training and complete logistics support. IAS tailors solutions to customer cost, performance, and schedule requirements and designs to consistently exceed expectations – with an unrivaled record of on time and on (or under) budget deliveries.
Role Expectations Specific to JTF Sierra
- Translate broad mission objectives into program\-level AI/ML architectures, strategies, staffing plans, data approaches, and technical frameworks.
- Drive system\-level AI/ML decision\-making by establishing technical standards and guiding engineering trade studies that shape platform\-level autonomy and perception capabilities.
- Identify and champion high\-value R\&D opportunities, emerging technologies, and cross\-organizational partnerships that accelerate SNC’s AI/ML advancements.
- Provide deep technical consultation across JTF Sierra and adjacent Business Unit product lines, ensuring architectural coherence and consistent technical excellence.
- Ensure AI/ML solutions are architected for scalability and seamless integration with enterprise\-wide platforms. Collaborate with IT and infrastructure teams to define and implement the necessary tools, data pipelines, and computing resources to support sustainable AI/ML operations across the organization.
- Balance program\-specific AI/ML solution development with the strategic creation of reusable frameworks, shared data assets, and foundational infrastructure that enable cross\-program and enterprise\-wide AI/ML adoption.
Responsibilities:
- Lead design and technical direction for next\-generation architectures spanning deep learning, reinforcement learning, multimodal generative AI, and advanced perception/decision systems
- Architect and oversee end\-to\-end multi\-program AI/ML systems across platforms and embedded systems
- Assist with development of long\-term technical strategies, roadmaps, and requirements for emerging AI/ML initiatives
- Identify, define, and advocate for the foundational data, compute, MLOps, and cloud/on\-prem infrastructure necessary to support sustainable and secure AI/ML development and deployment across JTF Sierra and related business units.
- Establish and promote best practices for the full AI/ML lifecycle—including data management, model versioning, CI/CD for ML, monitoring, and continuous improvement—to ensure reliable deployment and operation of AI/ML models in production.
- Oversee multiple development streams, providing technical reviews, risk assessments, and mitigations plans
- Shape system\-level behavior and engineering tradeoffs when requirements are ambiguous
- Lead development of simulations, sensor fusion models, vision models, and planning/decision algorithms
- Represent JTF Sierra to leadership, customers, and partners (assist in developing and presenting briefings, demos, high\-level technical presentations, etc)
- Establish adaptive, agile AI/ML validation, verification, and safety frameworks for proof\-of\-concept level mission\-critical systems
- Evaluate and introduce emerging technologies (examples: transformers, RLHF, edge AI, XAI, GPU acceleration)
- Partner with Program Manager and Project Engineer to define staffing, data, schedules, and resources required to execute JTF Sierra technical initiatives
- Coach and develop engineering talent, raising JTF Sierra’s overall AI/ML capabilities
Qualifications You Must Have:
- Bachelor’s degree in Computer Science, Engineering, Math, Statistics, or related STEM field
- 14\+ years of experience in AI/ML or related fields, or 16\+ years without a degree
- Demonstrated mastery of deep learning, reinforcement learning, generative models, and large\-scale AI/ML system architecture
- Proven experience architecting and deploying mission\-critical and/or large\-scale AI/ML systems
- Strong proficiency in Python, C\+\+, C\#, and/or Java with experience building scalable Machine Learning systems
- Experience providing technical leadership across teams, projects, or programs
- Ability to define technical strategies, influence senior stakeholders, and make organization\-level architecture decisions
- In\-depth experience with aerospace/defense\-relevant regulatory and cybersecurity considerations
- Demonstrated ability to mentor and grow engineering talent within an organization
Qualifications We Prefer:
- Advanced degree (MS or PhD) in AI/ML or related field
- Experience applying AI/ML to autonomy, multimodal sensor fusion, or embedded/real\-time platforms
- Experience establishing or scaling ML engineering standard (MLOps, validation frameworks, data management)
- Expertise with GPU acceleration, CUDA/TensorRT, or parallel computing
- Publications, patents, or thought leadership in AI/ML
- Familiarity with edge AI, explainable AI (XAI), or emerging/advanced ML topics
- Experience translating high\-level mission objectives into complex AI/ML system architectures (HMI scenarios, autonomy stacks)
Essential Functions:
- Define and lead complex AI/ML projects across multiple programs
- Collaborate in a hybrid environment while supporting rapid demonstration timelines
- Travel 10\-30% for R\&D, customer and partner engagement, field testing, and conferences
This posting will be open for application for a minimum of 5 days and may be extended based on business needs.
SNC offers annual incentive pay based upon performance that is commensurate with the level of the position.
SNC offers a generous benefit package, including medical, dental, and vision plans, 401(k) with 150% match up to 6%, life insurance, 3 weeks paid time off, tuition reimbursement, and more .
IMPORTANT NOTICE:
This position requires the ability to obtain and maintain a Secret U.S. Security Clearance. U.S. Citizenship status is required as this position needs an active U.S. Security Clearance for employment. Non\-U.S. citizens may not be eligible to obtain a security clearance. The Department of Defense Consolidated Adjudications Facility (DoD CAF), a federal government agency, handles the adjudicative aspects of the security clearance eligibility process for industry applicants. Adjudicative factors which affect the outcome of the eligibility determination include, but are not limited to, allegiance to the U.S., foreign influence, foreign preference, criminal conduct, security violations and illegal drug use.
SNC is a global leader in aerospace and national security committed to moving the American Dream forward. We’re known and respected for our mission and execution focus, agility, and disruptive and rapid innovation. We provide leading edge technologies and transformative solutions that support our nation’s most critical security needs. If you are mission\-focused, thrive in collaborative environments, and want to make our country stronger with state\-of\-the\-art technologies that safeguard freedom, join our team!
SNC is an Equal Opportunity Employer committed to an environment free of discrimination. Employment decisions are made based on merit without regard to race, color, age, religion, sex, national origin, disability, status as a protected veteran or other characteristics protected by law.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Sierra Nevada Corporation, 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 $181,170 based on 12,692 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sierra Nevada Corporation AI Hiring
Sierra Nevada Corporation has 5 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span Sparks, NV, US, Lone Tree, CO, US, Remote, US. Compensation range: $98K - $171K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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