Interested in this AI/ML Engineer role at Anduril?
Apply Now →About This Role
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century's most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril's family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.
ABOUT THE TEAM
CorpTech Platform is the internal engineering force multiplier behind Anduril's corporate systems.
It drives CorpTech engineering forward through strategic investment in data platforms, software platforms, QA and release excellence, ERP engineering, and AI infrastructure.
We build the foundations that power both CorpOS, enabling Finance and Growth to operate with speed and precision, and ArsenalOS, the digital backbone of Anduril's hardware enterprise. By unifying systems, accelerating development velocity, and embedding intelligence into every layer, CorpTech Platform transforms how the company operates end to end.
We are also driving Anduril toward becoming an autonomous enterprise. Through initiatives like the Autonomous Software Factory, we are rethinking how software is built, tested, deployed, and evolved by integrating AI directly into the engineering lifecycle.
This is not a support function. It is a strategic engine. CorpTech Platform is how Anduril scales its business systems with the same rigor, speed, and adaptability as its mission critical products.
WHAT YOU'LL DO
- Act as the quality and reliability lead within a small, senior AI development pod focused on solving novel, high-impact business and engineering problems.
- Design and implement test strategies, validation approaches, and release readiness criteria for AI-enabled software, automation, and agentic workflows.
- Partner closely with software and AI engineers to identify failure modes early across code, prompts, models, integrations, infrastructure, and user workflows.
- Build confidence in fast-moving solutions through automated testing, observability, instrumentation, environment design, and operational safeguards.
- Help define practical standards for reliability, resiliency, debugging, and production operations in an AI-first development model.
- Contribute directly in code, infrastructure, and tooling where needed to improve delivery confidence, developer feedback loops, and production stability.
- Support launch readiness, production issue response, root cause analysis, and continuous improvement for front-facing systems delivered by the pod.
- Evangelize strong engineering discipline in areas such as quality assurance, release engineering, infrastructure hygiene, and incident prevention without sacrificing speed.
REQUIRED QUALIFICATIONS
- 5+ years of experience in software engineering, site reliability engineering, quality engineering, infrastructure engineering, or a closely related role in a fast-paced environment.
- Demonstrated experience building or operating reliable production software systems, including ownership of testing, observability, deployment confidence, and operational readiness.
- Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or platform infrastructure.
- Experience working with frontier AI tooling, AI coding assistants, LLM-enabled applications, or agentic systems, including awareness of the unique quality and reliability challenges these systems introduce.
- Ability to move between hands-on implementation and systems-level quality strategy, with sound judgment on where rigor is required versus where speed is appropriate.
- Strong debugging, root cause analysis, and incident response instincts, with a bias toward preventing classes of failures rather than repeatedly reacting to them.
- Excellent written and verbal communication skills, with the ability to influence senior engineers and cross-functional stakeholders on quality and reliability trade-offs.
- Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience.
- U.S. Person status is required as this position needs to access export controlled data.
PREFERRED QUALIFICATIONS
- Experience supporting AI-first or AI-accelerated software development teams, including designing quality controls around non-deterministic system behavior.
- Experience with automated testing strategies spanning unit, integration, end-to-end, performance, and reliability testing for complex software products.
- Experience with infrastructure as code, cloud platforms, observability stacks, release engineering, and production operations.
- Experience in hyper growth startup-like environments, with demonstrated success balancing speed, ambiguity, and engineering rigor.
- Familiarity with enterprise systems and business process domains such as ERP, MES, WMS, CRM, finance systems, or manufacturing systems.
- Eligible to obtain and maintain a U.S. Secret security clearance.
The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:
Benefits
At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you're supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits*.*
Protecting Yourself from Recruitment Scams
Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.
To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:
- No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.
- Please always verify communications: + Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. + Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to [email protected].
- Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links.
- What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to [email protected]. Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts.
Data Privacy
To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/.
By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Salary Context
This $146K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Anduril, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $146K to $194K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Anduril AI Hiring
Anduril has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Costa Mesa, CA, US. Compensation range: $194K - $253K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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