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About This Role
Worker Type
Regular Job Description
\*\*\*\*\*\*About the Role
AeroVironment is seeking a Senior AI/ML Engineer to support the development and deployment of machine learning algorithms that detect and classify drone threats for our Counter\-UAS systems. You will work with large\-scale RF datasets, applying DSP techniques for denoising and preconditioning spectrograms, building data pipelines, and identifying opportunities to automate manual workflows. This role sits at the intersection of signal processing, statistics, deep learning, and MLOps, working closely with our San Diego AI/ML team, SW engineers, and field test teams to train models, improve data quality, and scale our detection capabilities.
\*\*\*\*\*\*Responsibilities
- Help with the development, application, and evaluation of machine learning algorithms to address challenging detection and signal processing problems
- Analyze and manage large datasets to support model training and testing
- Identify opportunities to automate data processing workflows and improve team efficiency
- Apply Digital Signal Processing (DSP) principles to assist in algorithm development
- Analyze results from real\-world data to identify root causes, statistical trends, and insights
- Collaborate with multidisciplinary teams in research and development efforts
\*\*\*\*\*\*Required Qualifications
- Master's degree in Machine Learning, Computer Science, Applied Math, or similar field with 3 years of relevant experience
- Strong foundation DSP principles and techniques
- Proficiency in Python; MATLAB experience is a plus
- Work experience with machine learning, statistical signal processing, or pattern recognition
- Strong data handling skills and instincts for working with large, complex datasets
- Curiosity and motivation to experiment with new approaches and automation strategies
- Good written and verbal communication skills
\*\*\*\*\*\*Preferred Qualifications
- Hands\-on experience with PyTorch or similar deep learning frameworks
- Familiarity with MLOps practices (model training, versioning, deployment)
- Experience with spectral analysis, adaptive filtering, or detection/estimation theory
- Demonstrated ability to propose creative solutions to data or workflow challenges
- Exposure to Counter\-UAS, radar, EW, or defense RF systems
- Passion for building impactful products that serve real\-world missions
Clearance Level
No Clearance
The salary range for this role is:
$123,500 \- $188,500
AeroVironment considers several factors when extending an offer, including but not limited to, the location, the role and associated responsibilities, a candidate’s work experience, education/training, and key skills.
ITAR Requirement:
*This position requires access to information that is subject to compliance with the International Traffic Arms Regulations (“ITAR”) and/or the Export Administration Regulations (“EAR”). In order to comply with the requirements of the ITAR and/or the EAR, applicants must qualify as a U.S. person under the ITAR and the EAR, or a person to be approved for an export license by the governing agency whose technology comes under its jurisdiction. Please understand that any job offer that requires approval of an export license will be conditional on AeroVironment’s determination that it will be able to obtain an export license in a time frame consistent with AeroVironment’s business requirements. A “U.S. person” according to the ITAR definition is a U.S. citizen, U.S. lawful permanent resident (green card holder), or protected individual such as a refugee or asylee. See 22 CFR § 120\.15\. Some positions will require current U.S. Citizenship due to contract requirements.*
Benefits: AV offers an excellent benefits package including medical, dental vision, 401K with company matching, a 9/80 work schedule and a paid holiday shutdown. For more information about our company benefit offerings please visit: http://www.avinc.com/myavbenefits.
We also encourage you to review our company website at http://www.avinc.com to learn more about us.
Principals only need apply. NO agencies please.
About AV:
AV isn’t for everyone. We hire the curious, the relentless, the mission\-obsessed. The best of the best.
We don’t just build defense technology—we redefine what’s possible. As the premier autonomous systems company in the U.S., AV delivers breakthrough capabilities across air, land, sea, space, and cyber. From AI\-powered drones and loitering munitions to integrated autonomy and space resilience, our technologies shape the future of warfare and protect those who serve.
Founded by legendary innovator Dr. Paul MacCready, AV has spent over 50 years pushing the boundaries of what unmanned systems can do. Our heritage includes seven platforms in the Smithsonian—but we’re not building history, we’re building what’s next.
If you're ready to build technology that matters—with speed, scale, and purpose—there’s no better place to do it than AV.
*We are proud to be an EEO/AA Equal Opportunity Employer, including disability/veterans. AeroVironment, Inc. is an Equal Employment Opportunity (EEO) employer and welcomes all qualified applicants. Qualified applicants will receive fair and impartial consideration without regard to race, sex, color, religion, national origin, age, disability, protected veteran status, genetic data, sexual orientation, gender identity or other legally protected status.*
ITAR
U.S. Citizenship required
Salary Context
This $123K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At AeroVironment, 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. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $123K to $188K.
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.
AeroVironment AI Hiring
AeroVironment has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $188K - $188K.
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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