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About This Role
Conversational AI Designer
Position Summary
Do you want to be part of a collaborative User Experience and Conversational AI team helping to shape the future of consumer experience? As a Conversational AI Designer, you will help design and build omnichannel conversational experiences for our digital customers, including interactions for intelligent virtual assistants and chatbots. In this role, you will contribute to conversational design work under the guidance of senior designers while partnering with product, technology, research, and business teams to deliver clear, useful, and customer-centered experiences.
You are
A Conversational AI Designer with related, applicable experience in designing conversational AI assistants, AI agents, chatbots, and/or digital self-service experiencesf or customer-facing journeys.
We expect to see your passion for creating engaging and intuitive customer experiences, along with your ability to simplify complex topics into clear conversational interactions. To be successful in this role, you will demonstrate a solid understanding of user experience practices and conversational design principles, with the ability to apply those principles to real customer problems. You can work effectively with cross-functional partners to support the development and improvement of data-driven and AI-enabled digital assistants across products and services. You should be comfortable communicating ideas clearly, asking thoughtful questions, taking feedback, and managing assigned design work with appropriate guidance.
You have
Required:
- Three or more (3+) years of previous experience as a Conversational Designer or Experience Designer of AI tools/platforms
- One or more (1+) years of professional experience working with chatbots, agents, virtual assistants, voice assistants, or similar digital self-service experiences
- Experience with to IVR, speech recognition, or other multimodal conversational channels is a plus
- Knowledge of conversational AI technologies and dataset elements, including LLM, NLU, NLP, ML, utterances, prompts, intents, entities, and annotations
- Experience with Generative AI technologies, large language models, prompt design, and emerging AI capabilities
- Comfortable with working with complex data sets and working with APIs
- Experience with, conversational AI and digital assistant platforms such as Sierra, Google Dialogflow, IBM Watson, Rasa, Kore.ai, Salesforce Agentforce, Microsoft Copilot Studio or similar tools
- Ability to use design and documentation tools including Voiceflow, Adobe Suite, Figma, diagramming apps, Microsoft 365 suite, and Jira
- Knowledge for applying omnichannel design practices and customer touchpoints including websites, mobile apps, IVR/telephony, SMS, Apple iOS, Google Android, and voice assistant devices
- Experience working in Agile teams and collaborating within iterative product development environments
- Familiarity with Design Thinking methodology
- A background in user experience design, customer service, content management, brand and marketing, product design, UX writing, or NLP data science
You will
- Create conversational dialog designs, prompts, scripts, and interaction flows for human-machine conversations with guidance from senior design partners
- Work closely with product teams and business partners to support the planning, design, optimization, and delivery of virtual assistants in an agile development environment
- Partner with developers and technology teams by applying a working understanding of conversational AI platforms, design requirements, and implementation constraints
- Apply user-centered design methods to iterate through concepts that can be tested and validated with users
- Use data, analytics, and user feedback to inform design recommendations and improve conversational experiences
- Apply human-centered design approaches to frame problems, identify patterns, explore opportunities, and visualize possible solutions
- Collaborate within product teams to assess opportunities, constraints, and features while advocating for the end user and balancing business and technology needs
- Partner with researchers, product owners, and senior designers to participate in user research, testing, and metrics analysis that informs decisions and design improvements
- Continue building your knowledge of conversational AI, UX practices, information architecture, research methods, and platform capabilities, and apply that learning to assigned design work
- Support the measurement of design solutions by helping capture and interpret qualitative and quantitative data that informs experience improvements
Reporting Relationships
As a Conversational AI Designer, you will report to our Lead Conversational AI & UX Designer, who reports to our Head of Enterprise Experience Design with a dotted line to our Lead Conversational AI UX designer. organization.
Location
- The primary location for this position is Hudson Yards, NYC, Bethlehem PA, or Holmdel NJ. and hybrid remote.
Travel
- 10% Travel required for this position to alternate office locations.
Salary Range:
$81,580.00 - $122,365.00 The salary range reflected above is a good faith estimate of base pay for the primary location of the position. The salary for this position ultimately will be determined based on the education, experience, knowledge, and abilities of the successful candidate. In addition to salary, this role may also be eligible for annual, sales, or other incentive compensation.
Our Promise
At Guardian, you’ll have the support and flexibility to achieve your professional and personal goals. Through skill-building, leadership development and philanthropic opportunities, we provide opportunities to build communities and grow your career, surrounded by diverse colleagues with high ethical standards.
Inspire Well-Being
As part of Guardian’s Purpose – to inspire well-being – we are committed to offering contemporary, supportive, flexible, and inclusive benefits and resources to our colleagues. Explore our company benefits at www.guardianlife.com/careers/corporate/benefits. Benefits apply to full-time eligible employees. Interns are not eligible for most Company benefits.
Equal Employment Opportunity
Guardian is an equal opportunity employer. All qualified applicants will be considered for employment without regard to age, race, color, creed, religion, sex, affectional or sexual orientation, national origin, ancestry, marital status, disability, military or veteran status, or any other classification protected by applicable law.
Accommodations
Guardian is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Guardian also provides reasonable accommodations to qualified job applicants (and employees) to accommodate the individual's known limitations related to pregnancy, childbirth, or related medical conditions, unless doing so would create an undue hardship. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact [email protected]. Please note: this resource is for accommodation requests only. For all other inquires related to your application and careers at Guardian, refer to the Guardian Careers site.
Visa Sponsorship
Guardian Life is not currently or in the foreseeable future sponsoring employment-based visas (e.g., such as an H-1B). In order to be a successful applicant, you must be legally authorized to work in the United States, without the need for employer sponsorship/support now or at any time in the future.
Notice Regarding Guardian’s Use of Artificial Intelligence in Recruitment
As part of Guardian’s job application process, Guardian may use artificial intelligence tools (“AI Tools") to automate the sorting and filtering of information provided by applicants as part of its preliminary screening. This preliminary screening may be used to help identify applicant materials and resumes relative to their indication that the applicant meets the requirements for the specific job for which they are applying, as specified in the listing posted on Guardian’s jobs website (Careers at Guardian at https://www.guardianlife.com/careers). At Guardian, we do not use AI Tools to substantially assist or replace human judgment or discretionary decision making in our hiring process. All hiring decisions will be made by Guardian colleagues.
Please be aware that if you apply for a specific position with Guardian, you will have the choice of opting out of Guardian’s use of AI Tools during the job application process. If you would like to request an alternative process that does not utilize AI Tools or would like to request a reasonable accommodation, within ten business days of your position application, you must email your request to [email protected], making sure to provide your name and job requisition identification number. Guardian will retain your applicant materials and resume and all information therefrom in accordance with Guardian’s document retention policy, a copy of which you may request via [email protected].
Additionally, at applicable times, Guardian will make public the most recent bias audit results for such AI tools, which may be found here.
Current Guardian Colleagues: Please apply through the internal Jobs Hub in Workday.
Salary Context
This $81K-$122K range is in the lower quartile 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 Guardian Life, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($101K) sits 52% below the category median. Disclosed range: $81K to $122K.
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.
Guardian Life AI Hiring
Guardian Life has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Holmdel, NJ, New York, NY. Compensation range: $122K - $250K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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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