Interested in this AI/ML Engineer role at Newpage Digital Healthcare solutions?
Apply Now →Skills & Technologies
About This Role
Location: Remote \| Type: Contract
About Newpage Solutions
Newpage Solutions is a global digital health innovation company helping people live longer, healthier lives. We partner with life sciences organisations which include, pharmaceutical, biotech and healthcare leaders, to build transformative AI and data driven technologies addressing real\-world health challenges.
From strategy and research to UX design and agile development, we deliver and validate impactful solutions using lean, human\-centered practices.
We are proud to be a ‘Great Place to Work®’ certified company for the last three consecutive years. We also hold a top Glassdoor rating and are named among the "Top 50 Most Promising Healthcare Solution Providers" by CIOReview.
As an organisation, we foster creativity, continuous learning and inclusivity, creating an environment where bold ideas thrive and make a measurable difference in people’s lives.
Your Mission
We are hiring Front\-End Engineers to build the surfaces where AI outputs meet real users—streaming chat, dense dashboards, multi\-view visualizations, and the interaction patterns that make non\-deterministic systems feel trustworthy.
You are not implementing fixed designs. You are co\-shaping how people actually interact with what AI produces—across audiences with different mental models on the same product. You partner closely with product, design, and engineering to translate ambiguous ideas into interfaces that ship.
You treat AI as the substrate of how software gets built—not a tool to be cautious of, not something you are "exploring," but the medium you work in. You work fluently with Claude Code, Cursor, and modern AI\-assisted development workflows
What You’ll Do
Shape the surface
- Sit with product, design, and engineering to translate evolving requirements and ambiguous ideas into shipping interfaces.
- Co\-shape interaction patterns for AI\-driven experiences—streaming responses, evidence citations, confidence cues, guided workflows—as UX is stress\-tested with real users.
- Build prototypes fast to react to. Iterate from rough designs and feedback rather than waiting for finished specs.
Distinguish AI\-surfaced output from authoritative or human\-approved content visually and structurally—because in enterprise contexts that distinction matters.
*
Build (fast) with AI
- Build production\-grade front\-ends in React 18\+ with TypeScript, using Next.js (App Router) or equivalent meta\-frameworks (Remix, TanStack Start).
- Implement streaming UI patterns for chat interfaces and agent interactions—Server\-Sent Events, streaming fetch, optimistic updates, partial rendering.
Build dense, real\-time dashboards with multi\-level drill\-down (portfolio segment entity* task) and multi\-view visualizations.
- Implement data visualizations using Recharts, Visx, D3\.js, Plotly, or ECharts—including radar/spider charts, time\-series, and custom chart compositions.
- Build component architectures grounded in design systems and tokens, using Tailwind CSS with shadcn/ui, Radix UI, Material UI, or Mantine.
- Implement client and server state cleanly: TanStack Query for server state; Zustand, Redux Toolkit, or Jotai for client state.
- Use AI\-assisted development tools (Claude Code, Cursor, GitHub Copilot) through structured workflows, templates, and sub\-agents—with discipline and review.
Ship, Operate, Harden
- Own role\-based access at the UI layer, integrate with enterprise SSO (Okta, Auth0, Entra ID, or equivalent) using the appropriate SDK.
- Implement forms with React Hook Form \+ Zod (or equivalent schema validation) for type\-safe data flow end\-to\-end.
- Write tests that hold up: Vitest or Jest, React Testing Library, Playwright for E2E.
- Build to WCAG 2\.1 AA conformance: keyboard navigation, ARIA patterns, accessible colour and contrast, screen reader support.
- Deploy on AWS (or Cloudflare for edge use cases)—chosen for fit.
- Apply engineering practices that hold up in production: structured logging, observability, performance budgets, automated CI/CD (GitHub Actions).
- Own what you build end\-to\-end, including how it behaves in production.
What You Bring
- 3\+ years modern front\-end engineering experience, production\-grade.
- Strong React 18\+ and TypeScript fundamentals, with hands\-on Next.js (App Router) or equivalent meta\-framework experience.
- Data visualization experience: radar/spider charts, multi\-level drill\-down dashboards, real\-time data views. Hands\-on with at least one of Recharts, Visx, D3\.js, Plotly, or ECharts.
- Streaming UI experience: Server\-Sent Events, streaming fetch, real\-time data patterns, chat\-style interfaces with progressive rendering.
- Component architecture and design\-system implementation. Hands\-on with Tailwind CSS and at least one component library (shadcn/ui, Radix UI, Material UI, or Mantine).
- Strong state management with TanStack Query plus one of Zustand, Redux Toolkit, or Jotai.
- Forms and validation: React Hook Form \+ Zod or equivalent.
- Testing discipline: Vitest or Jest, React Testing Library, Playwright (or equivalent) for E2E.
- WCAG 2\.1 AA conformance, keyboard navigation, ARIA patterns.
- Active, structured use of AI\-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows.
- Comfortable iterating from evolving designs and co\-shaping interaction patterns rather than executing fixed specs.
- Strong attention to information hierarchy—you can hold multiple audiences with different mental models in your head on the same product.
- A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions.
- A no\-compromise attitude on clean code, performance, accessibility, and user experience.
- Bachelor's or Master's in Computer Science, Design Engineering, or a related discipline (or equivalent demonstrable experience).
Bonus Skills / Experience
-----------------------------
- Experience building front\-ends for LLM\-powered products: streaming chat UIs, evidence citation surfaces, agent interaction patterns.
- Design tokens and design\-system implementation experience at scale.
- Storybook for component documentation.
- Real\-time data patterns: WebSockets, server\-driven UI, optimistic updates.
- Authentication flows with enterprise SSO providers (Okta, Auth0, Entra ID, or equivalent).
- Healthcare or life sciences domain exposure.
Public writing, talks, or threads about building with AI.
*
What We Offer
At Newpage, we’re building a company that works smart and grows with agility, where driven individuals come together to do work that matters. We offer:
- A people\-first culture \- Supportive peers, open communication and a strong sense of belonging
- Smart, purposeful collaboration \- Work with talented colleagues to create technologies that solve meaningful business challenges
- Balance that lasts \- We respect your time and support a healthy integration of work and life
- Room to grow \- Opportunities for learning, leadership and career development, shaped around you
- Meaningful rewards \- Competitive compensation that recognises both contribution and potential
Ready to Apply?
Let’s build the future of health together. Apply below or reach out to:
#### More about Newpage
Newpage is a digital health solutions company. We devote ourselves to advancing the quality of life by enhancing health and optimizing the longevity of people. We do this by, passionately building futuristic technologies for global organizations across the healthcare ecosystem. We partake at every stage from problem definition, strategy \& service design, user research, UX design, and agile software development – utilizing lean practices to deliver and validate highly innovative digital health solutions that drive user value and business transformation.
Newpage is recognized by ‘CIO’s Review’ as “Top 50 Promising Healthcare Solution Providers” and Great Place to Work Certified (GPTW) 2023 \& 2024\.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Newpage Digital Healthcare solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Newpage Digital Healthcare solutions AI Hiring
Newpage Digital Healthcare solutions has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.