Interested in this AI/ML Engineer role at Linda Werner & Associates, Inc.?
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Help shape the experience behind the next generation of AI powered internal tools.
We're looking for a versatile UI/UX Designer to elevate the usability, design quality, and visual identity of a growing portfolio of AI powered tools.
You'll join a Product Operations team working across multiple high priority initiatives and play a key role in making sure the products being built are not only functional and reliable, but intuitive, polished, visually cohesive, and easy to adopt.
This is a highly collaborative, hands on role with ownership across UX research, product design, prototyping, visual design, branding, and design systems. You'll work directly with users, builders, product partners, and stakeholders to uncover friction, turn insights into thoughtful experiences, and create the design foundation that connects multiple products into one cohesive portfolio.
What You'll Do
Design Research & User Insights | 40%
- Bring products directly to users, including internal teams and product partners, to gather structured feedback and uncover opportunities for improvement
- Plan and conduct usability testing for existing and in development AI tools
- Translate research findings into clear, prioritized product and design recommendations
- Identify adoption friction, usability gaps, and unmet user needs
- Build and maintain a research repository that can help inform future product and roadmap decisions
Product Design & Prototyping | 25%
- Turn research and user needs into thoughtful, actionable product experiences
- Create user flows, wireframes, design specifications, high fidelity mockups, and interactive prototypes
- Design new products and features while improving existing experiences
- Lead design reviews and provide recommendations for proposed product changes
- Use prototypes to create stakeholder alignment and accelerate decision making before development
Visual & Graphic Design | 20%
- Help define and own the visual identity across a portfolio of internal products
- Create logos and visual identities for internal tools and services
- Develop graphics, illustrations, interface assets, and supporting visual content
- Design marketing and launch materials that help drive awareness and adoption of new products
Design Systems | 15%
- Build and maintain an internal design system from components and interaction patterns to style guidelines and reusable standards
- Create visual and interaction consistency across multiple products
- Develop documentation that enables builders and team members to create cohesive experiences without requiring a designer at every step
Front End Contribution
- Contribute directly to lightweight builds through vibe coding, particularly for marketing pages, brand experiences, and focused front end projects
- Help translate design intent into polished user experiences
- Establish design standards and patterns that can be applied across vibecoded web applications
Collaboration & Enablement
- Partner closely with builders, Product Operations team members, product partners, and cross functional stakeholders
- Translate user needs and adoption challenges into clearly scoped design opportunities
- Manage multiple concurrent projects and design requests while prioritizing work based on impact
- Document research methods, design standards, and system patterns so teams can apply them independently
Required Qualifications
- 3 to 5+ years of UI/UX or Product Design experience, ideally supporting internal tools, enterprise products, or B2B experiences
- Strong portfolio demonstrating experience across UX research, interaction design, product design, and visual or graphic design
- Advanced proficiency with Figma, including design specifications, prototyping, components, and design systems
- Experience establishing design standards for modern web applications, including vibecoded experiences
- Ability to manage multiple products and design requests simultaneously while prioritizing based on business and user impact
- Strong communication and collaboration skills with the ability to clearly document design decisions and standards
- Self directed and comfortable ramping quickly in a fast moving environment
Portfolio links must be included on the resume.
Preferred Qualifications
- Experience building or maintaining a design system from the ground up
- Experience designing AI powered, generative AI, or agent based products
- Familiarity with front end frameworks and component libraries
- Experience creating brand identities and launch materials for product rollouts
- Experience designing internal tools used by technical, operational, or product teams
This is more than a traditional UI/UX design position. You'll have the opportunity to influence how an expanding portfolio of AI tools looks, feels, and works.
You'll move between research, product thinking, visual design, prototyping, and design systems while partnering directly with the people building and using these products.
For a designer who enjoys creating structure where it doesn't yet exist, solving complex UX problems, and helping define how AI powered products should be experienced, this role offers significant ownership and visibility.
Key Skills
UI/UX Design • Product Design • UX Research • User Research • Usability Testing • Figma • Wireframing • Interactive Prototyping • High Fidelity Mockups • Design Systems • Component Libraries • Interaction Design • Visual Design • Graphic Design • AI Product Design • Generative AI • Agent Based Products • Internal Tools • B2B Products • Vibe Coding • Front End Design • Brand Identity • Product Launch Design
Location: Remote
Role type: Contract 6 Month Position
Expected hours: 40 per week
Benefits:
- Dental insurance
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Schedule:
- 8 hour shift
- Monday to Friday
Application Question(s):
- Do you or will you in the future require any sponsorship to work in the US?
Language:
- English (Required)
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Linda Werner & Associates, Inc., 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Linda Werner & Associates, Inc. AI Hiring
Linda Werner & Associates, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, New York, NY, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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