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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
The team runs a shared observability platform for LLM-based applications, currently built on Langfuse. This role owns it as a product: what capability it offers, how it is configured, and where it goes next. That means deciding which of the platform's features are enabled and why, how organisations, projects and access are structured, and what the defaults are for retention, sampling and redaction. It means judging at each upgrade which new upstream capability is worth adopting, rather than only keeping the version current, and setting that capability up properly when it lands so it is genuinely usable rather than merely switched on. It also means looking ahead: assessing additional or replacement backends as the estate grows and requirements change, running the spikes, and making a recommendation with evidence behind it. Keeping the platform running is a separate role. This one is judged on what the platform can do for the teams using it, and on whether the configuration decisions behind it still look right a year later. It is a hands-on technical role. The person is expected to configure, test and evaluate the platform themselves, and to arrive at a view rather than collect requirements and pass them on.
What You’ll Do
Own what the platform offers.
- Decide which features are enabled, for whom, and why, and keep that decision under review as the platform and its users change.
- Own the tenancy model: how organisations, projects and environments are structured, how access maps onto existing teams, and what the naming and ownership conventions are.
- Set the defaults that shape the data, including retention, sampling, payload trimming and redaction, balancing what is useful to keep against what should never be stored.
- Define what the platform exposes to the systems around it, so the integrations built against it have a stable and deliberate surface.
Keep the capability current.
- Track upstream releases and decide what is worth adopting, distinguishing genuinely useful capability from noise
- Configure and test new capability before it is offered to teams, including piloting it with a willing team where the change is significant.
- Retire configuration and features that are not earning their place.
- Work with the reliability engineer on upgrades: this role decides what to adopt and how it should be configured, while execution of the migration and its rollback sits with them.
Look ahead.
- Assess additional or replacement backends as requirements grow. Langfuse is the current backend and the assumption is that it stays, but the role is expected to know where it falls short and what else the market offers, whether for telemetry storage, metrics or evaluation.
- Run the spikes and proofs of concept, define the criteria in advance, and make recommendations that show the working rather than the conclusion alone.
- Understand the cost and scaling consequences of configuration and architecture choices, and make them explicit when recommending a change.
- Document what the platform offers and when to use what, so teams can serve themselves rather than asking.
What You Bring
- Hands-on ownership of an observability or developer-tooling product, as the person who configured and administered it rather than a user of it. Langfuse is ideal, another LLM observability product is close behind, and a comparable APM, tracing or analytics platform is acceptable.
- Configuration and administration of self-hosted software at organisation scale, covering tenancy, rolebased access, retention and data lifecycle.
- A working grasp of telemetry data models, including traces, spans, attributes and sampling, and OpenTelemetry conventions, sufficient to reason about what the platform is storing and why.
- Familiarity with LLM application architecture, including prompts, retrieval, tool calling, agents and evaluation, sufficient to judge which capability is worth enabling and what the resulting data is good for.
- Technology evaluation. A track record of assessing options against criteria set in advance, running a proof of concept, and writing a recommendation that a decision-maker could act on.
- Data governance judgement. Retention, redaction, personal data and access control, and the ability to hold a position on what should not be captured.
- Enough Kubernetes, Helm and GitOps literacy to make configuration changes through the delivery pipeline rather than by hand, and to discuss changes credibly with the engineers who run the platform.
- Working directly with engineering teams to understand what they need, then making a decision and explaining it, including to the teams who wanted something else.
- Clear written communication, since the output includes configuration standards, guidance and recommendations that others rely on without the author present.
- Nice to have Experience administering a self-hosted Langfuse deployment, or LangSmith, Arize Phoenix or Braintrust at a comparable level.
- Nice to have OpenTelemetry collector configuration, including pipelines, processors and exporters.
- Nice to have SQL and comfort querying an analytical store directly to understand platform usage.
- Nice to have Experience owning a platform through a vendor or backend migration.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $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.
Newpage Digital Healthcare solutions AI Hiring
Newpage Digital Healthcare solutions has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Remote, 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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