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Head of Applied AI Engineering
Full time
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Remote | United States | Israel
10 - 20 Years of Experience
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Head of Applied AI Engineering
Location
United States — Remote, working US time zones Israel — Remote, working Israel hours
Remote-first role with approximately 10–20% travel and quarterly team offsites in New York. No relocation support.
Company Stage of Funding
Growth-stage, profitable AI company
Founded: 2017
Total Funding: $50M+
Team Size: Approximately 100 employees
Industry: AI, Healthcare, Life Sciences
Business Scale: $100M+ revenue, with an ambition to scale to $500M.
Office Type
Remote-first. Candidates must be based in the United States and work US time zones, or be based in Israel and work Israel hours. The company maintains a small New York office and holds quarterly team offsites in New York. Expect approximately 10–20% travel to support enterprise clients, team collaboration, and strategic business needs.
Salary
$300,000–$365,000 base salary.
The company is flexible on compensation and may go higher for exceptional candidates. In addition to base salary, the role includes an annual performance-based bonus and competitive equity.
Equity
Competitive equity package.
The company offers periodic secondary equity sales approximately every 18–24 months, allowing employees to sell a portion of their vested shares before an IPO or other liquidity event. The stated program allows employees to sell up to 20% of vested shares during these events, subject to the applicable program terms.
Visa
Open to visa transfers, including OPT and H-1B transfers.
Candidates must be able to work from the United States in US time zones or from Israel in Israel hours. No relocation support is available.
Employment Type
Full-time
Company Description
This company is building an AI execution platform for healthcare, life sciences, and wellness. Its mission is to turn fragmented healthcare data into precise, measurable action within the enterprise systems organizations already use.
Healthcare organizations have access to extensive patient data, treatments, and scientific research, but executing on that information remains difficult. The platform connects data, AI, and operational workflows to help healthcare enterprises move from insight to action.
The company supports healthcare organizations, life sciences companies, and wellness businesses across complex workflows. Its work spans patient engagement, scheduling and routing, conversational AI, patient recruitment for clinical trials, and predictive targeting. Rather than simply providing dashboards or analytics, the platform integrates AI capabilities into existing enterprise systems to drive measurable outcomes.
The company reports supporting more than 190 million patients and members, generating over $2 billion in measurable value, and contributing to the development and launch of more than 50 life-changing drugs. It is a profitable, growth-stage business with a significant opportunity to expand its impact as healthcare organizations adopt AI-driven execution.
The organization has approximately 100 employees and operates across AI, healthcare, and life sciences. Its technical stack includes Python, TypeScript, Node.js, SQL, PostgreSQL, Redis, LangChain, LangGraph, AWS, S3, Kubernetes, SageMaker, Spark, Iceberg, ClickHouse, Anthropic, and Amazon Bedrock. Its work also involves healthcare systems such as EHRs and EMRs, with an emphasis on HIPAA and enterprise-grade data handling.
The company is entrepreneurial and ownership-driven. It values high-agency builders who move quickly, operate with low ego, and take responsibility for both product outcomes and business impact. Engineers are expected to make meaningful architectural decisions, work directly with customers, and translate complex business needs into production AI systems.
The Head of Applied AI Engineering will establish and lead a new Applied AI, or forward-deployed engineering, group. This person will be a hands-on technical leader who sets the standard for how applied AI engineers build and deploy systems while growing a high-performing team.
The role is intentionally weighted toward individual technical contribution: approximately 75% hands-on engineering and 25% people leadership. The ideal candidate is an experienced engineer and manager who can build production-grade agentic systems, work directly with strategic enterprise clients, and help scale the company's AI capabilities alongside its business.
What You Will Do
- Build and Ship Production AI Systems
Spend approximately 75% of your time building, designing, and shipping production AI systems.
Set the technical standard for how forward-deployed engineers approach architecture, implementation, testing, deployment, and iteration.
Design and build agentic AI systems that solve complex operational problems in healthcare and life sciences.
Develop production-grade LLM applications that integrate with enterprise systems and support real customer workflows.
Build systems that move beyond prototypes and demonstrations into reliable, measurable production use.
Make pragmatic architectural decisions that balance speed, reliability, scalability, and business value.
Establish engineering patterns and implementation practices that the broader Applied AI Engineering team can adopt.
Remain deeply hands-on while providing technical direction to other engineers.
- Develop Agentic AI and Conversational Systems
Build agentic AI systems and LLM-powered applications for enterprise healthcare workflows.
Develop voice agents and multimodal conversational systems for use cases such as patient scheduling, routing, and engagement.
Integrate conversational AI with client enterprise systems, operational workflows, and existing data infrastructure.
Use technologies such as Python, TypeScript, Node.js, LangChain, and LangGraph to build and orchestrate AI-powered applications.
Design systems that can reason over relevant information, execute actions, and interact reliably with external services.
Improve the reliability, accuracy, observability, and performance of AI agents operating in production.
Evaluate LLM capabilities, orchestration approaches, and system architectures to determine the right solution for each use case.
Ensure AI systems are designed for real-world enterprise constraints, including data access, privacy, reliability, and operational requirements.
- Build Predictive AI and Machine Learning Systems
Develop and ship predictive models that turn large-scale healthcare data into actionable outcomes.
Build precision-targeting capabilities using the company's approximately 1.2 petabytes of proprietary data.
Apply machine learning and data science to use cases such as identifying suitable patients for Phase II and Phase III clinical trials.
Work with large, complex datasets to develop useful predictions and support operational decisions.
Build the infrastructure and workflows required to deploy, monitor, and improve predictive models in production.
Collaborate across data science, engineering, and customer-facing teams to translate business problems into ML solutions.
Evaluate model performance against practical outcomes, not just offline metrics.
Ensure predictive systems can integrate into existing enterprise workflows and support measurable customer impact.
- Integrate AI Into Enterprise Healthcare Systems
Build AI applications that connect with existing healthcare, life sciences, and enterprise systems.
Integrate solutions with EHRs, EMRs, databases, APIs, and other client technology infrastructure.
Design service integrations that allow AI systems to access relevant information and execute actions within existing workflows.
Work with technologies such as PostgreSQL, Redis, SQL, AWS, and S3 to support data access and application requirements.
Build secure, reliable integrations that account for enterprise constraints and operational dependencies.
Understand customer workflows and identify where AI can improve execution, efficiency, and outcomes.
Work with client teams to navigate technical complexity, data availability, and system integration requirements.
Ensure solutions are practical to deploy, maintain, and scale across enterprise environments.
- Establish and Lead the Applied AI Engineering Team
Build and lead a new forward-deployed Applied AI Engineering group.
Recruit, hire, mentor, and develop a high-performing team of Applied AI Engineers.
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