Interested in this AI/ML Engineer role at Shield AI?
Apply Now →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:
This Director owns the full demand\-to\-value pipeline: sensing where the business needs help, separating signal from noise, identifying the highest\-leverage opportunities, deciding when to buy versus build, driving adoption, and proving measurable ROI.
This role is the AI team's primary operator with non\-engineering functions across Shield, and is expected to build and lead the team that executes this pipeline at scale. Success is defined by turning scattered interest into a focused portfolio of business\-backed use cases, converting pilots into real adoption, and demonstrating hard value in ways that earn trust, budget, and follow\-on demand.
While this role is business\-facing rather than a technical builder, it requires genuine technical grounding in machine learning and generative AI. Sound buy/build calls, credible use\-case shaping, and defensible ROI cases all depend on the leader understanding what these systems can and can't do, how they're evaluated, and where the real technical risk sits — not just what vendors claim.
### What you'll do:
Technical Grounding for Business Decisions
- Apply working technical knowledge of ML and generative AI — including model capabilities, limitations, training/tuning basics, and evaluation approaches — to pressure\-test vendor claims, size technical risk in buy/build calls, and shape use cases that are technically realistic.
- Distinguish generative AI from traditional ML in business conversations, since the two carry different technical risk profiles, build timelines, skill requirements, and cost structures, and translate that distinction into sharper prioritization and roadmap decisions.
- Partner with engineering and technical evaluators as a credible counterpart during vendor evaluation and market scans, able to interrogate technical claims rather than relay them.
- Incorporate model performance, drift, and technical limitations into ROI frameworks and success metrics, so measured value reflects real system behavior, not just adoption numbers.
Demand\-to\-Value Pipeline
- Own the single demand pipeline across Workplace AI: intake, shaping, prioritization, buy/build recommendation, rollout, adoption, and value measurement.
- Partner directly with business leaders, including senior executives, to surface needs, pressure\-test requests, and distinguish real opportunities from low\-value noise.
- Translate ambiguous business problems into clear requirements, decision\-ready use cases, and a prioritized portfolio of AI opportunities.
- Identify where AI can create the most leverage, whether through productivity, cycle\-time reduction, quality improvement, risk reduction, or better decision support.
- Lead build\-versus\-buy decisions, including market scans, vendor evaluation, recommendation development, and ownership of purchased\-solution rollout.
- Establish baselines, define success metrics, and build the ROI cases that show whether a solution is working after deployment.
- Own adoption as seriously as selection: partner with the business on training, behavior change, communications, and local champion models so tools actually stick.
Leadership \& Strategy
- Serve as the senior business\-facing counterpart to the VP, integrating business pull and technical push into one clear prioritization and operating rhythm.
- Build, lead, and develop a team responsible for demand shaping, triage, and business\-facing execution, establishing standards and processes that scale as the function matures.
- Prepare decision memos, frame tradeoffs, and resolve most prioritization questions before they reach the VP.
- Act as the trusted AI advisor to non\-engineering leaders, communicating in the language of outcomes, constraints, and business value.
- Own the strategic roadmap and budget for the demand\-to\-value function, aligning it to enterprise priorities over multi\-quarter horizons.
### What success looks like:
- Business units see Workplace AI as a trusted partner that helps clarify problems, not just respond to tickets.
- Buy/build decisions are faster, sharper, and better grounded in business value, technical reality, and market conditions.
- Deployed tools have defined baselines, measurable adoption, and defensible ROI.
- Training and change management are treated as core parts of delivery, not afterthoughts.
- Reliably handles shaping, triage, and business\-facing execution functions, with a team scaled to meet demand.
### Required qualifications:
- 15\+ years in business transformation, product, strategy, operations, enterprise technology, or related leadership roles.
- 5\+ years leading cross\-functional programs or transformations that required influencing senior stakeholders and driving behavior change, including direct people\-management experience.
- Substantive technical fluency in modern AI and generative AI — including how models are trained, tuned, and evaluated, and where their practical limitations sit — sufficient to assess vendor claims critically, shape technically realistic use cases, and hold a credible technical conversation with engineering counterparts. This is a genuine requirement, not surface familiarity, even though the role is not a hands\-on technical builder.
- Demonstrated ability to distinguish generative AI from traditional ML in terms of technical risk, skill requirements, cost, and timeline, and to apply that distinction to prioritization and staffing decisions.
- Proven track record translating business needs into deployed technology solutions with measurable business impact.
- Strong judgment on build versus buy decisions, including vendor evaluation, external market scanning, and procurement partnership.
- Experience defining baselines, KPIs, and ROI frameworks for new tools, workflows, or transformation efforts.
- Exceptional executive communication skills, especially with non\-technical leaders and sponsors.
- Proven track record building, leading, and developing high\-performing teams, including hiring and mentoring talent.
- Strong operator instincts: comfortable with ambiguity, able to create structure where little exists, and willing to own outcomes rather than hand off work.
### Preferred qualifications:
- Experience in internal AI, automation, digital transformation, or enterprise productivity programs.
- Background in consulting, high\-growth operating roles, or business\-side product leadership.
- Experience leading vendor\-backed implementations as well as internally built solution rollouts.
- Familiarity with change management, enablement, and adoption measurement in enterprise environments.
- Change\-management certification or equivalent demonstrated practice.
$280,000 \- $420,000 a year
\#LF
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.
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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 $280K-$420K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($350K) sits 60% above the category median. Disclosed range: $280K to $420K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Shield AI AI Hiring
Shield AI has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Melbourne, FL, US. Compensation range: $420K - $420K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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