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About This Role
Associate Application Engineer – AI \& Data
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Department: AI, Data \& Innovation
FLSA Type: Exempt
Reports To: Senior Director of AI \& BI
Location: Remote (Must reside within the contiguous United States)About PaceMate
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PaceMate is a remote cardiac device monitoring company that plays a critical role in healthcare by enabling continuous monitoring of patients with cardiac devices. We are a growing, PE\-backed healthcare technology company focused on building scalable, intelligent systems that improve operational efficiency and patient outcomes.
As PaceMate evolves beyond traditional reporting and analytics, the AI, Data \& Innovation team is focused on building internal applications, automation platforms, AI\-enabled workflows, and scalable data products that support the future of healthcare operations.The Opportunity
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This role is designed for a career driven technical professional with strong problem\-solving ability, curiosity, and an interest in building modern applications, automation systems, integrations, and AI\-enabled solutions. The ideal candidate is excited by technology, enjoys learning how systems work together, and wants to grow their skills in application development, automation, and scalable data architecture.
We are looking for a highly technical builder who can own applications, automate processes, solve complex system problems, and help scale the next generation of internal platforms and AI\-enabled solutions across the organization.
You will work across analytics applications, automation systems, AI workflows, integrations, and operational tooling. This role is ideal for someone who wants to grow from a junior\-level developer into a well\-rounded engineer capable of supporting applications, automation, and intelligent systems.
The ideal candidate is someone who naturally thinks:
“How do we build this better, automate this, and scale this long\-term?”
—not someone focused solely on dashboards or manual reporting.What Makes This Role Different
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Growth \& Hands\-On Development
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You will contribute to the development, maintenance, and support of internal applications, automation systems, integrations, and data\-driven platforms that directly support business operations and future innovation initiatives. This role focuses on application development, system reliability, automation, and scalable data architecture rather than traditional reporting or analytics support functions.AI\-First Development Environment
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AI is embedded into how we build. You will use modern AI tooling, agentic workflows, and automation frameworks to accelerate development, debugging, testing, and operational efficiency.Innovation\-Focused Team
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This role sits within the AI, Data \& Innovation organization—not traditional Business Intelligence. The expectation is continuous improvement, experimentation, and building scalable solutions that evolve with the business.High Impact Across the Organization
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You’ll work across multiple domains including:* Internal operational applications
- AI\-enabled workflows and automation
- Data platforms and integrations
- Analytics and reporting systems
- Salesforce and operational tooling
- Emerging innovation initiatives
What You’ll Do
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Application Development \& Support (50%)
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- Support the development, enhancement, and maintenance of internal applications and automation systems
- Assist with application reliability, performance improvements, and ongoing support activities
- Develop APIs, integrations, workflows, and backend services that support business operations
- Troubleshoot and resolve bugs, system failures, and performance bottlenecks
- Contribute to improving application maintainability and operational efficiency over time
- Participate in feature development, testing, and modernization initiatives
Data \& Platform Engineering (30%)
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- Develop and optimize data models, queries, and integrations across platforms including MySQL, PostgreSQL, Salesforce, and internal systems
- Build scalable automation and data\-processing solutions
- Ensure integrity, consistency, and reliability of operational and analytical data flows
- Support AI\-enabled workflows and automation pipelines
- Implement monitoring, validation, and observability practices across systems
AI, Automation \& Innovation (20%)
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- Leverage AI\-assisted development tools and agentic workflows to accelerate delivery and reduce manual effort
- Identify opportunities to automate operational processes and improve system efficiency
- Partner with leadership and cross\-functional teams to prototype and deliver innovative solutions
- Evaluate emerging technologies and recommend scalable implementations
- Learn and contribute to scalable engineering and automation best practices
What We’re Looking For
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Core Skills (Non\-Negotiable)
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### 1\. Curiosity \& Builder Mentality
- You enjoy learning how systems and applications work and finding ways to improve them
- You naturally look for opportunities to improve, automate, and scale processes
- You are dependable, proactive, and willing to learn through hands\-on experience
### 2\. Technical Problem Solving
- Strong debugging and troubleshooting skills across applications, data, and integrations
- Comfortable learning and working across applications, APIs, databases, and automation workflows
- Ability to troubleshoot issues and work through technical problems with guidance and collaboration
### 3\. AI\-Assisted Development Experience
- Experience using AI tools such as Claude CLI, Cursor, ChatGPT, GitHub Copilot, or similar
- Comfortable incorporating AI into development workflows, debugging, automation, and documentation
- Understanding of prompt engineering and agentic development concepts
- Curiosity and willingness to continuously adopt emerging tools and technologies
### 4\. Development \& Automation Skills
- Foundational proficiency in Python, PHP, or similar programming languages
- Familiarity with SQL and relational databases such as MySQL or PostgreSQL
- Experience building projects, scripts, applications, or automation solutions through work, school, or personal projects
- Familiarity with JavaScript and modern web application concepts
- Interest in learning scalable development practices and maintainable coding standards
### 5\. Data \& Systems Awareness
- Strong understanding of data structures, relationships, and operational workflows
- Experience working across interconnected systems and integrations
- Interest in understanding how systems, data, and automation work together
Experience Level
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- 1–3 years of experience in software development, automation, data, IT, or similar technical roles
- Equivalent internship, project, bootcamp, or hands\-on experience will also be considered
- Experience supporting applications, scripts, integrations, or automation projects preferred
- Experience in healthcare technology, operational systems, or data\-intensive environments is a plus
Technical Environment
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Languages \& Databases
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- Python
- PHP
- JavaScript
- MySQL
- PostgreSQL
Platforms \& Systems
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- Salesforce
- APIs \& integration frameworks
- Cloud and automation tooling
- Internal operational systems
AI \& Automation Tooling
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- Claude
- Cursor
- AI\-assisted coding tools
- Agentic development workflows
- Automation frameworks
Bonus Experience (Preferred, Not Required)
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- AWS, Azure, or GCP
- Databricks or Snowflake
- Docker or containerized applications
- CI/CD pipelines
- Healthcare interoperability standards (HL7, FHIR)
- AI workflow orchestration tools
- Frontend frameworks and modern UI development
What Success Looks Like
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First 30 Days
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- Understand PaceMate’s systems, architecture, and operational workflows
- Contribute bug fixes, enhancements, or automation improvements
- Demonstrate proficiency with AI\-assisted development workflows
- Build relationships across engineering, operations, and leadership teams
First 90 Days
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- Begin independently contributing to internal applications and automation system
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- Deliver measurable improvements in automation, performance, or operational efficiency
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- Reduce manual operational workload through scalable technical solutions
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- Contribute to the AI, Data \& Innovation roadmap
First Year
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- Grow into a trusted technical contributor across multiple systems and applications
- Improve platform scalability, reliability, and maintainability
- Continue developing technical expertise in AI\-assisted development and automation practices
- Accelerate organizational innovation through automation and intelligent tooling
Our Values in Action
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- Impact\-focused: Everything we build ultimately affects patient care and operational excellence
- Innovation\-driven: We embrace emerging technologies and modern engineering approaches
- Ownership: We take responsibility for outcomes and long\-term success
- Pragmatic: We build scalable solutions that create measurable value
- Collaborative: We solve problems together and continuously learn from one another
Why Join PaceMate’s AI, Data \& Innovation Team?
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- Ground\-floor opportunity to help shape the future of AI and innovation at PaceMate
- High\-impact technical work with visible organizational influence
- AI\-first engineering culture using modern development approaches
- Opportunity to build applications and systems that directly improve healthcare operations
- Remote\-first flexibility with a highly collaborative team
- Ability to influence architecture, tooling, and future technical direction
Qualifications
An individual must be able to perform each Essential Function of the job satisfactorily. Reasonable accommodation may be made to enable individuals with disabilities, who are otherwise qualified, to perform the essential functions. Nothing within this job description restricts management’s right to assign or reassign duties and responsibilities to this job at any time.
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Physical Requirements
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- While performing the duties of this job, the employee is consistently required to remain in a stationary position, constantly operate a computer and other office equipment, and frequently communicate with customers and employees. Consistently remain in a stationary position working at a computer
- Constantly operate a computer and other office equipment
- Frequently communicate with team members via voice and video
- Exchange accurate information using voice over internet protocol
- Perform repetitive motions for programming and data entry
- Home office setup: Maintain a dedicated, separate office or room at home to ensure privacy and professional working conditions.
- Connectivity: Maintain a reliable, high\-speed internet connection at your residence.
- Must be able to exchange accurate information, with the ability to effectively utilize voice over internet protocol; and repetitive motions using fingers and forearms in data entry.
- Geographic restriction: Must reside and work exclusively within the contiguous United States.
- Must legally be eligible to work in the United States.
- Notification protocol: You must inform and receive approval from the IT department before working from any location other than the legal address PaceMate has on file. To update your address or request approval for a temporary work location, contact PaceMate IT or your HR representative. Additional information is available in the PaceMate official handbook.
- Foreign transport of sensitive information: When confidential information is carried into a foreign country, it must be stored in an inaccessible form (e.g., encrypted external storage) or remain in the worker’s possession at all times.
- PaceMate workers may not take secret PaceMate information into another country without permission from Physical Security management.
Equal Opportunity
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PaceMate is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
This job description outlines the general nature and level of work expected. It is not an exhaustive list of all responsibilities, duties, and skills required. PaceMate reserves the right to modify job duties or job descriptions at any time.
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Salary Context
This $130K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PaceMate™, 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. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($145K) sits 20% below the category median. Disclosed range: $130K to $160K.
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.
PaceMate™ AI Hiring
PaceMate™ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
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
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