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About This Role
The AI/ML Engineer II is a mid\-level position for individuals with professional experience in designing and implementing machine learning algorithms. In this role, you will independently develop and deploy AI/ML solutions to address complex challenges, such as autonomous systems, predictive maintenance, and computer vision. You will take ownership of specific projects, perform data analysis, and optimize models for performance and scalability. This position requires a combination of technical expertise, problem\-solving skills, and the ability to collaborate with multidisciplinary teams to meet mission\-critical objectives.
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.
Responsibilities:
- Design, implement, and optimize machine learning models for applications such as object detection, signal processing, predictive analytics, and decision\-making systems.
- Develop and maintain data pipelines for collecting, preprocessing, and managing large\-scale datasets. Identify data gaps and propose solutions to improve data quality.
- Conduct performance testing and validation of AI/ML models using rigorous evaluation metrics. Optimize models for accuracy, efficiency, and scalability.
- Write and deploy efficient, modular code to integrate AI/ML models into operational systems, ensuring reliability and compatibility with existing platforms.
- Test AI/ML solutions in simulated environments to evaluate performance under real\-world conditions. Contribute to system\-level debugging and troubleshooting.
- Collaborate with hardware engineers, software developers, and systems architects to align AI/ML solutions with mission\-critical requirements.
- Document technical designs, workflows, and testing procedures for internal and external use. Share findings and best practices with team members.
- Explore and integrate emerging AI/ML frameworks, tools, and methodologies to enhance system capabilities and address new challenges.
- Train, evaluate, and optimize standard AI models (ANNs, CNNs, RNNs) for supervised and unsupervised tasks.
- Implement and test basic reinforcement learning algorithms and generative models under supervision.
- Develop and integrate signal processing and computer vision modules to enhance perception and decision\-making capabilities.
- Conduct simulations and performance profiling of AI/ML models on CPU/GPU architectures, identifying bottlenecks.
- Execute validation and verification procedures, analyze test results, and support system compliance with safety and reliability standards.
Qualifications You Must Have:
- Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
- 2\+ years of experience in a related field.
- Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 6 years of related experience is required.
- Higher level relevant degree may substitute for experience.
- Practical experience using machine learning frameworks (e.g., TensorFlow, PyTorch) and applying core AI/ML techniques, including supervised, unsupervised, and introductory reinforcement learning methods.
- Hands\-on experience implementing and evaluating ANNs, CNNs, and RNNs in small\-scale or pilot projects. Assisted with deploying machine learning models in production or research environments.
- Proficiency in programming languages such as Python, C\+\+, C\# or Java.
- Strong understanding of supervised and unsupervised learning techniques.
- Experience deploying AI/ML solutions in production environments.
Qualifications We Prefer:
- Master’s degree in Artificial Intelligence, Machine Learning, or related field. Experience with reinforcement learning or generative AI models (e.g., GANs, Transformers).
- Working knowledge of Agile or DevOps practices in software/ML project environments.
- Hands\-on experience with at least one advanced ML technique (e.g., clustering or dimensionality reduction) in coursework or projects.
- Basic experience with GPU programming (e.g., CUDA basics) or using GPUs for ML model training.
- Exposure to generative models (e.g., GANs, Transformers) or reinforcement learning frameworks.
- Experience analyzing and processing diverse datasets to extract insights.
- Familiarity with requirements gathering and basic deployment of ML systems.
- Awareness of hardware acceleration tools and edge AI concepts.
Essential Functions:
- Work extensively on a computer for coding, debugging, and integrating AI/ML systems.
- Travel occasionally to testing sites, customer locations, or conferences (up to 10\-20%).
- Ability to work in a hybrid environment and manage multiple tasks effectively.
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: $108,496\.89 \- $149,183\.22\. 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 $108K-$149K 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 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($128K) sits 29% below the category median. Disclosed range: $108K to $149K.
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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