Implementation Engineer

Storm3; New York (NY); On-site; USD 100,000 - 130,000; Full time

Job summary

Storm3 is seeking a Forward Deployed Engineer to join their team specializing in AI-native solutions for healthcare organizations. The role involves developing and implementing voice AI agents and managing complex, technical projects with direct customer interaction.

Ideal candidates have experience in engineering, project management, and customer relations, driving both product development and customer satisfaction.

Qualifications

• 3+ years of engineering experience in a customer-facing role.
• Ability to manage multi-workstream technical projects.
• Experience as a primary technical point of contact.

Responsibilities

• Developing integrations and configuring AI agents.
• Leading technical project plans from start to finish.
• Acting as the primary technical point of contact for customers.

Skills

Production system engineering

Project management

Technical communication

AI agent tuning

Job description

We are working with a rapidly growing AI-native healthcare company transforming how healthcare organizations manage patient communication and administrative operations. By combining cutting-edge voice AI with modern software infrastructure, they are building autonomous agents that handle complex workflows such as scheduling, referrals, billing inquiries, and patient support helping providers operate more efficiently while improving access to care.

They are looking for a Forward Deployed Engineer to join their growing team!

Responsibilities

• Build and ship: Write production-grade code - developing integrations, configuring AI agents, and implementing platform logic grounded in customer workflows and scheduling requirements.
• Own implementations end-to-end: Lead technical project plans from initial scoping through go-live. Align internal resources, delegate workstreams, and keep timelines on track.
• Engineer our moat: Our edge is our willingness to stay close to customers until the technology actually works. You'll design the high-leverage systems and playbooks that turn complex, one-off implementations into repeatable, scalable infrastructure.
• Be the technical face to customers: Act as the primary technical point of contact - leading discovery sessions, resolving integration blockers, and earning the trust of customer engineering teams.
• Optimise AI agents in production: Track agent performance at scale, identify failure patterns, and iteratively sharpen behaviour to improve accuracy, reliability, and patient outcomes.
• Grow accounts: Drive post-launch expansion by rolling out new features, spotting upsell opportunities, and getting ahead of emerging pain points before they become problems.
• Shape the product: Turn what you observe in the field into clear, actionable requirements that feed directly into the product roadmap.

Requirements

• Technical foundation: 3+ years of engineering experience shipping production systems in a customer-facing capacity. This is a hands-on coding role not project management with a technical title.
• Project leadership: Proven ability to manage complex, multi-workstream technical projects and coordinate across functions to hit deadlines.
• Customer-facing presence: Experience as a primary technical POC, comfortable leading architecture conversations and building credibility with technical stakeholders.
• Product intuition: Able to distil customer feedback into sharp insights that connect field reality to engineering priorities.

Interesting Technical Challenges

• AI agent tuning at scale: Configure voice agents for medical terminology and complex scheduling logic, then continuously refine as real-world edge cases surface in production.
• Workflow orchestration: Map and automate multi-step processes - scheduling, insurance verification, prescriptions, documentation across varied customer environments.
• Personalisation that scales: Deploy agents that adapt to each organisation's unique protocols and terminology without rebuilding from scratch for every customer.
• Debugging AI in the wild: Diagnose agent failures spanning integrations and LLM behaviour, then implement fixes that hold across deployments.
• Shape the product from the field: As the closest touchpoint to customers, spot patterns across deployments and translate them into product requirements that move the roadmap forward.