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About This Role
Company Overview
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ID.me is the next\-generation digital identity wallet that simplifies how individuals securely prove their identity online. Consumers can verify their identity with ID.me once and seamlessly login across websites without having to create a new login and verify their identity again. Over 152 million users experience streamlined login and identity verification with ID.me at 20 federal agencies, 45 state government agencies, and 70\+ healthcare organizations. More than 600\+ consumer brands use ID.me to verify communities and user segments to honor service and build more authentic relationships. ID.me's technology meets the federal standards for consumer authentication set by the Commerce Department and is approved as a NIST 800\-63\-3 IAL2 / AAL2 credential service provider by the Kantara Initiative. ID.me is committed to "No Identity Left Behind" to enable all people to have a secure digital identity. To learn more, visit https://network.id.me/.
ID.me is a full\-time, in\-office culture. Unless a specific job description explicitly states otherwise, all roles are on\-site five days per week at one of our offices in McLean, VA; Mountain View, CA; New York City, NY; or Tampa, FL. Certain roles — such as field\-based sales or other remote\-by\-design positions — may have different work arrangements as noted in their individual postings.
At ID.me, we embrace the thoughtful use of AI tools in our daily work and there are even occasions where we leverage AI in our hiring process. However, during the interview process, we want to understand your individual skills and experiences. Therefore, we have guidelines on how AI can be appropriately used during your application and interviews which can be found here.
Role Overview
We are seeking an AI Enablement Engineer to join our new Corporate AI Enablement \& Efficiency team — the group responsible for turning AI from a buzzword into everyday leverage for the entire company. You will design and build the automations, integrations, and AI agents that connect our applications, eliminate manual work, and help every team do more with the tools they already have. Your work will directly shape how a 1,000\-person organization adopts AI: safely, measurably, and at scale.
You will wire up production workflows, connect systems through APIs, and stand up MCP servers, AI skills, and agents that give teams reliable, governed access to large language models. Other days you will be out in the business, spotting the manual, repetitive work worth automating and shipping solutions that stick. Throughout, you will build like an engineer: real error handling, observability, evaluations, and guardrails — and you will keep Security and Data close so everything you ship stays compliant. \[Senior / P4 and above: you will also set the patterns, standards, and architecture the rest of the team builds on, and own our toughest build\-vs\-buy and platform calls.]
You will work at the frontier of applied AI: agents, MCP, retrieval, and orchestration with the autonomy to build real things that of colleagues. If you love turning messy manual processes into elegant solutions and want to help define how a company puts AI to work, this is an exciting opportunity to make a meaningful impact.
Role Responsibilities
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- Design, build, and maintain end\-to\-end automations and integrations, connecting the company's applications, data, and AI services.
- Integrate systems through APIs and webhooks — handling authentication, rate limits, retries, and idempotency.
- Build AI\-powered capabilities using large language models: structured outputs, retrieval\-augmented generation (RAG), and tool / function\-calling that automate real business processes.
- Develop and maintain MCP servers and reusable AI skills and agents that give teams safe, governed access to internal systems and data.
- Instrument everything you build with logging, monitoring, evaluations, and guardrails, and resolve failures before users feel them.
- Partner with teams across the company to identify high\-value automation and AI use cases, then scope, prioritize, and deliver them.
- Work with the Security and Data teams to ensure responsible, compliant handling of data, secrets, and AI usage.
- Contribute to build\-vs\-buy evaluations and help reduce software and license waste by consolidating or replacing tools with automation.
- Create clear documentation, templates, and enablement materials so your work scales beyond you.
- Set technical patterns, standards, and architecture for the team's automation and agent platform, and mentor other engineers.
- Own the architecture of the AI and automation estate and lead the team's most complex, cross\-functional initiatives. (Applies if the role is filled at the Lead level.)
- Perform other duties as assigned to meet team and organizational needs.
Required Skills / Abilities
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- 4\+ years building integrations, automations, or software in a professional environment including production work with LLMs or AI agents.
- Hands\-on experience with workflow\-automation.
- Strong API fluency (REST / JSON, OAuth, webhooks) and the ability to read and write code in Python, Go, JavaScript / TypeScript or similar.
- Practical experience building with large language models: prompting, structured outputs, and at least one of RAG, tool / function\-calling, or agents.
- A production mindset \- error handling, logging and observability, testing or evaluations, and secure handling of secrets and sensitive data.
- Ability to work directly with non\-technical colleagues: understanding a business process and translating it into a reliable, well\-documented automation.
- Understanding of data privacy, security, and responsible\-AI considerations, and a habit of partnering with Security and Data teams.
Ideal Qualifications
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- Experience building or deploying MCP servers, AI agents, or custom AI skills.
- Familiarity with the modern AI stack: orchestration frameworks, vector databases, and evaluation tooling.
- A portfolio of shipped automations or agents that shows production quality, not just prototypes.
- Experience integrating common business systems and their APIs (HRIS, ITSM, CRM, finance, and collaboration suites).
- Exposure to enterprise AI tools (AI copilots, chat assistants) and helping teams adopt them effectively.
- A track record of reducing software or license spend through automation or tool consolidation.
- Experience setting standards, mentoring engineers, or owning build\-vs\-buy and platform decisions.
- Strong written communication and documentation skills, and a bias toward making your work reusable.
ID.me maintains a work environment free from discrimination, where employees are treated with dignity and respect. All ID.me employees share in the responsibility for fulfilling our commitment to equal employment opportunity. ID.me does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. ID.me adheres to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, ID.me's policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works. Upon request we will provide you with more information about such accommodations.
Please review our Privacy Policy, including our CCPA policy, at id.me/privacy. If you provide ID.me with any personally identifiable information you confirm that you have read and agree to be bound by the terms and conditions set out in our Privacy Policy.
ID.me participates in E\-Verify.
Salary Context
This $128K-$143K range is in the lower quartile 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 ID.me, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($136K) sits 37% below the category median. Disclosed range: $128K to $143K.
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.
ID.me AI Hiring
ID.me has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $143K - $143K.
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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