Interested in this AI/ML Engineer role at Shield AI?
Apply Now →Skills & Technologies
About This Role
Founded in 2015, Shield AI is a venture\-backed defense\-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V\-BAT and X\-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia\-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.### Job Description:
The Staff Engineer, AI Engineering is a senior individual contributor responsible for translating the enterprise AI engineering roadmap into scalable platform architecture, reusable technical patterns, and production\-grade shared services. Reporting to the Director, AI Engineering, this role provides deep technical leadership across AI enablement, responsible AI controls, observability, cost attribution, and reusable component strategy. The Staff Engineer acts as the connective technical tissue across Engineering, IT, Security, Legal, Data, and business unit teams \- setting standards, creating reference implementations, and guiding teams toward consistent, secure, measurable AI adoption without relying on direct authority. Success is defined by high\-quality platform components adopted across teams, clear architecture and governance patterns, measurable productivity and cost outcomes, and effective mentorship of engineers building AI\-enabled capabilities.
### What you'll do:
AI Platform Architecture \& Standards
- Define and evolve enterprise AI architecture patterns for LLM integration, retrieval\-augmented generation, agentic workflows, prompt orchestration, and workflow automation.
- Create reference architectures, design reviews, decision records, and implementation guidance that enable consistent AI development across business units.
- Serve as a technical authority for AI platform decisions, including model selection, integration approaches, data boundary enforcement, and lifecycle management.
- Evaluate emerging AI technologies and recommend fit\-for\-purpose adoption paths aligned to security, operational, and enterprise architecture requirements.
- Partner with product, platform, and business technology teams to identify common needs and convert them into reusable engineering patterns.
Reusable Components \& Shared Services
- Design and build reusable AI components such as connectors, agents, skill templates, prompt libraries, data pipelines, integration adapters, and service APIs.
- Lead technical design for shared platform services for AI observability, logging, usage metering, evaluation, and lifecycle management.
- Establish quality, versioning, deprecation, documentation, and contribution standards for the shared AI component catalog.
- Guide teams through adoption of shared components, balancing standardization with practical implementation needs.
- Identify opportunities to eliminate duplicate AI engineering efforts through consolidation, abstractions, and platformization.
Responsible AI Engineering \& Governance
- Architect engineering controls for access management, data classification enforcement, prompt safety, output validation, audit logging, and policy adherence.
- Partner with Security, Legal, and compliance stakeholders to embed responsible AI requirements into development and deployment pipelines.
- Design model and agent lifecycle governance patterns, including version tracking, evaluation, drift monitoring, rollback, and deprecation workflows.
- Build technical dashboards and telemetry that expose adoption, risk, performance, and governance compliance across AI\-enabled systems.
- Represent engineering considerations in AI governance reviews and translate policy requirements into implementable technical standards.
Productivity, Measurement \& Technical Leadership
- Develop AI\-assisted workflow patterns that improve individual productivity, team collaboration, knowledge retrieval, meeting intelligence, document generation, and task automation.
- Design measurement approaches that connect AI usage to time savings, quality improvement, error reduction, capacity creation, and business value.
- Partner with Finance and platform teams to develop cost metering, showback/chargeback, and optimization mechanisms for AI services.
- Mentor senior and mid\-level engineers, raise engineering quality, and lead complex cross\-functional technical initiatives from concept through production.
- Contribute to communities of practice, internal enablement material, and technical evangelism for enterprise AI engineering standards.
### Required qualifications:
- Progressive experience in enterprise software engineering, AI platform engineering, data platform engineering, or digital workplace technology roles.
- Deep hands\-on understanding of generative AI, large language model integration, RAG architectures, agentic AI patterns, prompt orchestration, and production AI system design.
- Experience designing shared platform services, reusable component libraries, APIs, integration frameworks, or developer enablement platforms used by multiple teams.
- Strong architecture judgment across security, reliability, scalability, observability, maintainability, and operational cost tradeoffs.
- Experience implementing or contributing to AI governance controls such as access management, data classification, audit logging, model lifecycle management, and compliance\-aware development practices.
- Ability to influence technical direction across matrixed teams through architecture reviews, written guidance, reference implementations, and hands\-on collaboration.
- Experience defining metrics, telemetry, or attribution mechanisms for adoption, productivity, cost, quality, or operational performance.
- Strong written and verbal communication skills with the ability to explain complex AI engineering concepts to technical and non\-technical audiences.
### Preferred qualifications:
- Experience in regulated, defense\-adjacent, security\-sensitive, or data\-governed environments.
- Background in MLOps, AI observability, model evaluation frameworks, agent evaluation, and production monitoring.
- Familiarity with enterprise AI tooling ecosystems including copilot platforms, workflow automation suites, vector databases, and enterprise search/RAG platforms.
- Hands\-on experience with enterprise data platforms such as Databricks, Snowflake, lakehouse architectures, or comparable data foundations.
- Experience implementing usage metering, cost allocation, showback/chargeback, or AI spend optimization capabilities.
- Track record of mentoring engineers and raising technical standards without relying on direct management authority.
- Advanced degree in Computer Science, Engineering, Data Science, or a related technical field.
$190,000 \- $290,000 a year
\#LI\-KE1
\#LD
Full\-time regular employee offer package:
Pay within range listed \+ Bonus \+ Benefits \+ Equity
Temporary employee offer package:
Pay within range listed above \+ temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part\-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
\#\#\#
Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $190K-$290K range is above the 75th percentile 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 Shield AI, 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 ($240K) sits 12% above the category median. Disclosed range: $190K to $290K.
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.
Shield AI AI Hiring
Shield AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $290K - $290K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.