Interested in this AI/ML Engineer role at Sierra Nevada Corporation?
Apply Now →About This Role
Do you have strong analytical and communication skills, like to work in a collaborative environment, and have a background in the US Department of Defense, US Military, or Aerospace \& Defense industry? The Cybersecurity Analyst I will play a crucial role in identifying, evaluating, and remediating cyber threats under the guidance of the Cyber Security Manager. This entry\-level position focuses on monitoring SNC systems, networks, and software, analyzing logging and alerting data, and escalating potential security events. The role also involves collaborating with business users to support the integration of cybersecurity protections into business operations and participating in incident response activities.
As SNC's corporate team, we provide the company and its business areas with strategic direction and business support spanning executive management, finance and accounting, operations, human resources, legal, IT, information security, facilities, marketing, and communications.
Responsibilities:
- Conduct security assessments of AI technologies and their applications.
- Review and analyze software for potential vulnerabilities and security risks.
- Evaluate hardware configurations for security compliance and potential risks.
- Assess and review SaaS configurations to ensure they meet security standards.
- Identify and prioritize security vulnerabilities across systems and networks.
- Communicate with stakeholders to ensure identified vulnerabilities are effectively remediated.
- Collaborate with business users to integrate cybersecurity measures into business operations.
- Develop and maintain documentation related to security assessments and findings.
Stay up\-to\-date with the latest security trends, threats, and best practices, particularly in the context of AI technologies.
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Qualifications You Must Have:
- Bachelor's degree in a related field.
- 0\-2 in a related role.
- Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 4 years of related experience is required.
- Basic understanding of cybersecurity principles and practices.
- Familiarity with network security, threat analysis, and incident response.
- Knowledge of data security administration principles, methods, and techniques.
- Familiarity with domain structures, user authentication, and digital signatures.
- Ability to work well within a team environment
Excellent organizational skills
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Qualifications We Prefer:
- Relevant certifications such as CompTIA Security\+, Certified Ethical Hacker (CEH), or similar.
- Experience with cybersecurity tools like Security Information and Event Management (SIEM), Intrusion Detection System (IDS)/Intrusion Prevention System (IPS), and endpoint protection solutions.
- Strong analytical and problem\-solving skills.
- Ability to read and interpret security and technical documentation.
- Proven track record of maintaining the confidentiality of high\-sensitivity projects and data.
Ability to perform critical\-incident response.
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Essential Functions:
- Ability to work in an office or hybrid environment.
- Prolonged periods sitting at a desk and working on a computer.
- May require occasional lifting of up to 20 pounds.
- Ability to travel as needed.
This posting will be open for application for a minimum of 5 days and may be extended based on business needs.
Estimated Starting Salary Range: $71,338\.47 \- $98,090\.40\. Compensation varies depending on a wide array of factors, such as candidates' key skills, relevant work experience, and education/training/certifications. The disclosed range estimate may be adjusted for any applicable geographic differential associated with the location at which the position may be filled.
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.
Salary Context
This $71K-$98K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($84K) sits 53% below the category median. Disclosed range: $71K to $98K.
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.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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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