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About This Role
Element Science, Inc. is a medical device and digital health company redefining how patients with heart disease are monitored and treated as they transition from the hospital to the home. Our mission is to improve outcomes for patients at risk of life-threatening cardiac events through innovative solutions at the intersection of clinical-grade wearables, advanced algorithms, and therapy delivery.
We are entering an exciting phase of growth as we move from development into early commercialization. Our approach combines hardware, software, and services to deliver a more personalized and seamless patient experience—designed with both patients and clinicians in mind. Our product, a wearable patch defibrillator, is designed for patients with a temporary elevated risk of sudden cardiac arrest, addressing a significant and underserved population of over 500,000 patients in the U.S. By focusing on comfort, usability, and clinical effectiveness, we aim to transform the standard of care during this critical period. Headquartered in the San Francisco Bay Area, Element Science is backed by leading healthcare and technology investors, including Third Rock Ventures, GV (Google Ventures), Deerfield Healthcare, Cormorant Asset Management, and Invus Opportunities.
SUMMARY OF ROLE:
Element Science is seeking a hands-on Senior Manager, Integration, Data & AI Platforms to own the connective layer of our enterprise technology: the integrations that move orders, patient, clinical, billing, and financial data between our systems; the data platform that turns that data into reporting and insight; and the AI agents and internal applications that extend the capacity of our clinical, patient-services, revenue-cycle, and finance teams.
As we have moved from clinical trials into commercial operations, our business systems footprint has grown, and so has the need to connect those systems, trust the data that flows between them, and put AI to work where it can give our teams more capacity. Dell Boomi is our integration hub and Google Cloud is the platform for the applications, data, and AI services we build ourselves. Our multi-year roadmap spans phased system integrations, hospital EMR connectivity, an enterprise analytics platform, and a growing set of AI agents, all built within a HIPAA, FDA 21 CFR Part 11, SOC 2, and PCI compliance envelope.
This is a player-coach role. You will design and build alongside contractors and vendor partners today, set the technical standards and governance for this domain, and grow a small team as the roadmap scales. You will partner closely with our Business Systems team (who own application configuration) and our IT Operations & Security team (who own identity, endpoints, and security controls), and you will work directly with leaders in Commercial, Patient Services, Revenue Cycle, Clinical, Quality, and Finance to turn manual, spreadsheet-driven handoffs into connected, auditable, measurable workflows.
At Element Science, you will join a collaborative team advancing life-saving technology that improves patient outcomes. We are looking for someone who enjoys building from the ground up, who is as comfortable in a Boomi process or a Python service as in a roadmap review, and who wants to build a durable, well-governed data and AI foundation for a company that is scaling.
RESPONSIBILITIES:
Enterprise integration
- Own the enterprise integration architecture and delivery roadmap on Dell Boomi, including the phased integrations between Salesforce and our billing, clinical, ERP, payer, and contact-center systems.
- Lead hospital EMR connectivity: design the EMR integration platform and deliver onboarding waves with our commercial and clinical partners, using HL7 v2 and FHIR standards and appropriate interface partners.
- Build and maintain a documented interface catalog with data-flow, ownership, PHI classification, error handling, monitoring, and SLA definitions for every production interface.
- Establish integration standards, versioning, environments, change control, testing, logging, alerting, and runbooks that satisfy HIPAA, SOC 2, and, where interfaces touch validated systems, 21 CFR Part 11 change-control expectations.
Data platform and analytics
- Design and deliver the enterprise data platform on Google Cloud (BigQuery with a modern transformation and BI layer), starting with revenue-cycle and commercial operations reporting and expanding across the business.
- Define and implement data governance for a regulated healthcare environment: data classification (PHI / PCI / GxP), access controls, lineage, retention, data-quality monitoring, and audit evidence.
- Lead master data management for patients, prescribers, accounts, payers, and products, working with Business Systems and functional owners to establish sources of truth and stewardship.
- Partner with Finance, Commercial, Revenue Cycle, and Operations to deliver trusted dashboards and self-service reporting, and to retire manual spreadsheet-based reporting.
AI agents and internal applications
- Own the platform and practices for the AI agents and internal applications we build on Google Cloud, with a disciplined local staging production development flow.
- Deliver and operate production AI agents in partnership with business owners, beginning with order quality checks and patient and clinic communications, and extending to voice workflows and revenue-cycle automation, framed around team capacity, turnaround time, and cost-to-collect.
- Stay curious and hands-on as the AI landscape moves: evaluate emerging models, agent frameworks, and tooling; run small experiments before committing; and be willing to change the stack as we learn what works in our environment.
- Implement the practices that make AI safe to run in a regulated company: evaluation, human-in-the-loop review, version control for prompts and models, observability, PHI boundaries, and clear documentation of what each agent may and may not do.
- Contribute to the company's AI governance program and training efforts, including vendor and model assessments and the technical standards that keep protected data inside BAA-covered services.
Cloud platform, security, and compliance
- Own the IT-managed Google Cloud environment for this domain (IAM, networking, secrets, cost management, backup and recovery, infrastructure-as-code) in partnership with IT Operations & Security.
- Build for auditability from day one: maintain the design documentation, change records, access reviews, and monitoring evidence needed for HIPAA, SOC 2 Type II, PCI-scoped payment integrations, and Part 11 where applicable.
- Participate in vendor selection and diligence for integration, data, and AI tooling, and manage the day-to-day relationships with those vendors and implementation partners.
Leadership
- Direct and quality-review the work of contractors and implementation partners; hire, develop, and lead a small IT team in this domain as the roadmap scales.
- Translate business problems into a prioritized, sequenced roadmap; present plans, trade-offs, and progress to IT leadership and cross-functional stakeholders in clear, non-technical terms.
- Act as the technical counterpart and backup to the Head of IT & Security for this domain, including architecture decisions, roadmap ownership, and executive-level communication.
What Success Looks Like
- Within 90 days: you own the interface catalog and the in-flight Salesforce billing integration, with monitoring and change control in place for production interfaces.
- Within 6 months: the next integration phase is delivered, the analytics platform is live for revenue-cycle and commercial reporting, and the first production AI agent is operating under documented governance.
- Within 12 months: the EMR integration platform is in production with the first hospital onboarding wave underway, ERP integrations are delivered, and this domain passes internal SOC 2 readiness review with no significant findings.
QUALIFICATIONS:
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
- 8+ years in enterprise integration, data, or application development within an IT organization, including 2+ years leading technical staff, contractors, or a technical program end-to-end.
- Hands-on expertise with an enterprise iPaaS — Dell Boomi strongly preferred; MuleSoft, Workato, or Informatica considered — including production deployment, error handling, and monitoring.
- Strong Python and SQL; experience building and operating production services on a major cloud platform (Google Cloud preferred; AWS or Azure with willingness to work in GCP).
- Practical experience building LLM-based applications or agents for production use — orchestration, retrieval, tool use, evaluation, and observability — not just prototypes, paired with genuine curiosity about a field that changes monthly.
- Experience designing data warehouses and analytics platforms (BigQuery, Snowflake, or Redshift; modern transformation and BI tooling).
- Experience handling protected health information or other highly regulated data, with working knowledge of HIPAA technical safeguards and audit evidence expectations.
- Demonstrated ability to work as a player-coach in a small company: comfortable building things yourself, directing others, and communicating with non-technical business leaders.
Preferred
- Healthcare interoperability experience: HL7 v2, FHIR, EMR integration (Epic, Cerner / Oracle Health), and interface engines or integration partners such as Redox or Health Gorilla.
- Familiarity with the Salesforce data model and APIs (Health Cloud / Service Cloud), DME or medical billing systems, revenue-cycle workflows, and payer connectivity (Availity or similar).
- Experience integrating ERP systems (NetSuite preferred) and contact-center platforms (Amazon Connect preferred).
- Exposure to FDA 21 CFR Part 11 and computer system validation, SOC 2 Type II, or PCI-DSS in a small or mid-size company.
- Python web frameworks and modern front-end experience; infrastructure-as-code; CI/CD.
- Experience in medical device, digital health, or life sciences companies during commercial scale-up.
BENEFITS:
Element Science offers a very competitive salary and benefits package including, but not limited to:
- Stock Options
- 90% employer-paid medical, dental, and vision insurance
- Company-paid Basic Life Insurance
- 401(k) retirement plan (Traditional and Roth)
- Competitive Paid Time Off
- Paid Holidays
- FSA (Flexible Spending Accounts)
- HSA (Health Savings Account)
- Employee Assistance Program through PEO
The salary for this exempt-level position will be based on experience and qualifications within an established pay range.
- Pay range: $165,000 - $188,000
Element Science is an Equal Opportunity Employer. All candidates will be evaluated on the basis of their qualifications for the job in question. We do not base our employment decision on an employee's or applicant's race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, or other non-merit-based factors prohibited by local, state, or federal law.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. We are not able to sponsor individuals for employment visas for this job.**At this time, we are unable to offer relocation assistance at Element Science.
Salary Context
This $165K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Element Science, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $165K to $188K.
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.
Element Science AI Hiring
Element Science has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $188K - $188K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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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