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About This Role
Company Information
Clinical Ink is the global life science company that brings data, technology, and patient science together to unlock clinical discovery. Our deep therapeutic-area expertise, coupled with Direct Data Capture, eCOA, eConsent, telehealth, neurocognitive testing, and digital biomarkers advancement, drive the industry standard for data precision and usher in a new generation of clinical trials. With offices in the United States and Denmark, Clinical Ink is rewriting the clinical development experience.
Job Description
Clinical Ink is seeking an AI Engineer to join our Digital Health Technology (DHT) team based in the United States. As an AI Engineer on the DHT team, you will be at the center of that lifecycle - evaluating devices to ensure integration feasibility, then owning the production integration that ships it: mobile SDK and backend API integrations, device adaptors, ingestion pipelines, and the data model that takes it from prototype to a live, validated capability. You will also build the AI layer that makes each integration faster than the last, applying our AI-Driven Development Lifecycle (AI-DLC) to study ready builds, automated validation, and agentic workflows - equally comfortable writing the integration code yourself and building the tooling that accelerates the generation of integration patterns. The AI Engineer's responsibilities include but are not limited to:
- Innovate Device Integration Patterns + Design and build end-to-end technical integrations for new connected devices — mobile SDK and vendor API integrations, device adaptors, ingestion pipelines, and the APIs that align device data to our platform data model. + Develop reusable, therapeutic-area-agnostic integration and event/alert patterns that replace one-off, hardcoded builds and shorten time-to-integration for the next device. + Own technical design decisions under ISO 13485 design-control principles, carrying integrations through implementation, testing, and release.Apply and extend our AI-Driven Development Lifecycle (AI-DLC) to accelerate device integration, reference study builds, and automated validation, working closely with Engineering, Quality, Data, and Operations teams.
- Cross-Platform Data Pipelines + Build and maintain the data pipeline infrastructure that turns raw device streams into validated digital biomarkers, dashboards, and clinical endpoints. + Design pipeline patterns that generalize across our various platform environments and languages, rather than one-off, environment-specific solutions. + Integrate vendor SDKs and APIs to normalize and validate incoming data ahead of downstream analytics and reporting.
- Collaborate across DHT Teams + Partner with Innovation, Product, Engineering, Data and Program leads through our DHT stage-gate process to align team activities from evaluation to study-ready. + Work with Quality and Regulatory teams to validate integrations against ISO 13485 and GxP requirements before release, and with Operations to ensure builds are ready for study configuration, logistics, and support. + Document evaluation methodology, reference architectures, and repeatable integration patterns so every device that follows ships faster than the one before it.
Qualifications
- Bachelor's degree in information systems, computer science, engineering, or a related field or equivalent experience.
- 5+ years of software engineering experience shipping production systems (APIs, data pipelines, or platform integrations) and comfort owning integration work end-to-end, from technical evaluation through design, build, testing, and release.
- Background in digital health, connected medical devices, clinical trials, or biomedical/health data preferred.
- Understanding of medical device or regulated-software quality practices (ISO 13485, GxP, 21 CFR Part 11, or similar) or strong eagerness to learn our compliance model preferred.
- Strong Python engineering skills, with the versatility to work across additional languages and platform stacks (i.e., JavaScript/TypeScript, Java, .NET/C#) as integrations require.
- Experience with containerization (Docker) and deploying on Kubernetes (Helm, EKS).
- Experience with relational databases (PostgreSQL, SQLAlchemy).
- Experience with CI/CD and GitOps-based deployment (GitLab CI, ArgoCD).
- Experience designing data models and working with cloud data infrastructure (AWS preferred), event-driven (SQS, Kafka, Azure Event Hub) and batch-based processing with idempotency and deduplication, and data warehouse integration.
- Experience integrating mobile SDKs and vendor/device APIs into production applications (native iOS/Android or cross-platform), including device pairing, Bluetooth, and REST/vendor device API connectivity patterns.
- Hands-on experience building or integrating LLM-based or agentic systems (RAG, tool-use/function calling, multi-agent orchestration); familiarity with GraphRAG or knowledge-graph patterns and AWS Bedrock/AgentCore is a plus.
- Experience working with streaming or time-series sensor/device data (value + timestamp patterns), including ingestion, normalization, and basic signal processing.
Additional Information
Clinical Ink is an equal opportunity employer and does not discriminate against otherwise qualified applicants on the basis of race, color, creed, religion, ancestry, age, sex, marital status, national origin, disability or handicap, or veteran status. Personal data submitted in connection with this position will be processed by Clinical Ink Denmark ApS and Clinical Ink, Inc. for recruitment and hiring purposes. Paycor Recruiting is used to collect and manage applications; please review the Clinical Ink Privacy Policy and Paycor Privacy Policy for additional information.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Clinical Ink, 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Clinical Ink AI Hiring
Clinical Ink has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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