Lead AI Engineer, Stealth Longevity AI Startup
Bedrock Talent · Remote ·
- Work mode
- Remote
- Seniority
- Lead
- Category
- AI engineering
- Experience
- 6+ years
Bedrock Talent · Remote ·
Stage: Early-Stage, Pre-Series A Healthcare Technology Company
Reports to: CTO, with close collaboration with the COO
Location: Remote, with occasional travel as needed
Bedrock Talent is partnering with an early-stage healthcare technology company building an AI-first platform designed to generate clinically meaningful, explainable insights from complex healthcare data. The company is developing advanced AI and analytics capabilities across longitudinal datasets to help healthcare organizations make more informed decisions and improve outcomes.
Because the company is conducting this search confidentially, additional information will be shared with qualified candidates during the interview process.
The Lead AI Engineer will own end-to-end engineering execution, AI/ML system development, and technical leadership during a pivotal stage of the company’s growth.
This person will lead the design and delivery of the company’s AI-first platform, including agent-based architectures, data pipelines, model orchestration, and production systems supporting advanced cohort analysis and longitudinal insight generation.
This is a hands-on builder and technical leadership role. The Lead AI Engineer will translate product requirements, data science models, and scientific frameworks into scalable, secure, and production-ready systems.
The role works closely with Data Science, Product, Operations, Medical, and Research leaders to ensure the platform delivers clinically meaningful, explainable, and high-performing AI-driven insights.
Lead the design and implementation of the company’s AI-first platform architecture, including:
Agent-based systems, such as matching, reasoning, and orchestration agents
Model orchestration layers and inference pipelines
Data ingestion, transformation, and feature pipelines
Build and operationalize systems supporting:
Proprietary analytical framework execution
Cohort matching and comparative analysis
Longitudinal and temporal data analysis
Ensure systems are modular, extensible, and designed to support future scale
Own engineering execution and delivery across product initiatives and company milestones, including work completed with external resources and partners
Translate product requirements, PRDs, and user stories into clear technical plans and deliverables
Drive sprint planning, execution, and delivery in close partnership with Product
Establish a predictable release cadence while balancing speed, quality, and technical debt
Quickly understand, improve, and take ownership of existing production systems
Serve as the company’s senior hands-on engineering leader in partnership with the CTO
Make pragmatic architecture and technology decisions appropriate for an early-stage environment
Design systems for future scalability, security, and compliance without over-engineering
Establish technical standards, documentation, and engineering best practices
Help lead and mentor a small engineering team as the company grows
Partner closely with Data Science leadership to productionize models and analytical frameworks
Implement MLOps and LLMOps practices, including:
Evaluation harnesses
Prompt and version management
Retrieval-quality monitoring
Output grounding and citation integrity
Latency and token-cost monitoring
Ensure models and AI systems are:
Scalable and performant
Reproducible and testable
Observable and auditable
Reliable in production environments
Own engineering quality and QA execution throughout the development lifecycle
Establish testing practices for:
AI and machine-learning systems
Data pipelines
APIs and integrations
Full-stack product functionality
Implement CI/CD pipelines, automated testing, and release-readiness gates
Ensure system reliability, performance, and observability
Design and implement infrastructure for integrating with EHR data, claims data, longitudinal healthcare datasets, and relevant research or genomic data sources
Partner with Data Science and Product to ensure engineering practices support healthcare privacy and security expectations
Ensure the platform supports HIPAA-aligned data handling and security practices
Collaborate with Operations leadership on compliance-related technical requirements
Build secure, scalable integrations with external systems and data partners
Work closely with the CTO and COO to align engineering execution with company priorities
Partner closely with Product on requirements, scope tradeoffs, and delivery timelines
Collaborate with Data Science, Medical, and Research leaders to translate scientific direction into technical implementation
Support customer implementation and service teams with technical onboarding, integrations, and issue resolution
Communicate technical decisions, risks, and tradeoffs clearly to both technical and nontechnical stakeholders
Own release planning, deployment readiness, and rollback strategies
Monitor production performance, uptime, and system health
Lead incident response and postmortem processes
Drive continuous improvements in reliability, observability, and operational readiness
Contribute to lightweight operating rhythms, including planning sessions, execution reviews, and retrospectives
Surface technical and execution risks early and recommend practical solutions
Partner with Operations leadership on resourcing, sequencing, and delivery tradeoffs
Create enough structure to support reliable execution without slowing down an early-stage team
6+ years of software engineering experience, including full-stack web application development, relational database design, event-driven systems, and cloud-based production environments
Recent, meaningful experience using AI coding tools and agents, such as Claude Code or comparable platforms
Bachelor’s or master’s degree in computer science, or equivalent practical experience
Experience working on highly collaborative, small engineering teams within high-growth startup environments
Interest in helping lead and mentor a small team while remaining deeply hands-on
Experience building and productionizing LLM, generative AI, or other AI-driven applications
Experience in healthcare technology, digital health, or another regulated environment
Demonstrated success delivering production SaaS or platform-based products
Strong knowledge of modern software development practices, CI/CD, microservices, cloud infrastructure, and production operations
Ability to quickly understand and take ownership of existing production systems
Proven ability to operate effectively amid ambiguity, rapid change, and evolving priorities
Exceptional communication skills and genuine enthusiasm for working in a mission-driven environment
Enjoys collaborating closely with Product and Design
Experience working with Model Context Protocol tools or MCP-based environments
Familiarity with healthcare security, compliance, and audit requirements
Experience partnering with Product, Data Science, Medical, or Research teams
Experience helping a startup scale through the Series A or Series B stage
Experience with healthcare data sources, including claims, EHR, clinical, genomic, or longitudinal datasets
MVP and early product milestones are delivered on time
The company has a stable and reliable platform supporting early customers
QA, testing, and release discipline are embedded throughout the engineering lifecycle
Product, Data Science, Operations, and Engineering work together effectively
AI and analytical systems are explainable, testable, observable, and production-ready
The company has strong technical and operational foundations in place for Series A growth
Competitive base salary commensurate with experience
Meaningful equity participation aligned with the company’s stage and the role’s impact
Benefits package including healthcare coverage, paid time off, and professional development support