The job, in the employer's words
About the company
Our client is a fast-growing healthcare technology company.
The role
Raydar is recruiting for this role on behalf of our client. Act as the technical lead for customer implementations, working directly with client stakeholders from early demonstrations through go-live and beyond. The work blends software development, solution design and executive-level communication, with plenty of room for independent judgment.
What you'll do
- Take charge of large client projects across their full lifecycle, from initial demos and discovery to build, integration, rollout and support.
- Design and develop production software that connects to complex client systems while respecting privacy and regulatory obligations.
- Facilitate working sessions and present recommendations to senior leaders at client organizations.
- Decide on architecture and tooling on your own, frequently as the sole engineer present, defining integration scope and resolving issues as they arise.
- Bring insights from client work back to the product organization and create reusable building blocks that make later rollouts faster.
- Travel to clients for roughly 10-15% of your time.
Requirements
What we're looking for
- About 5 to 15 years of hands-on engineering, including running real production systems from design through operation rather than only prototypes.
- Skill at planning complete system designs and communicating them through clear diagrams.
- Background in healthcare or life sciences, ideally touching insurance, patient-facing services, clinical workflows or regulatory compliance.
- A record of leading technical work for external customers across the sales process, scoping and implementation.
- Experience delivering customer-facing software in a solutions engineering, solutions architecture, consulting or similar engineering role.
- Strong command of Python and TypeScript, plus cloud platform experience.
- Experience building data pipelines and machine learning workflows.
- Polished communication and presence with executives as well as technical and non-technical audiences.
- A founder-style mindset: high ownership, accountability for significant client outcomes and comfort making calls under pressure.
Bonus points
- Time spent in an early-stage or high-growth startup engineering environment, or as a founder or technical lead.
- Hands-on work with LLM-powered applications or AI agents.
- Formal machine learning or data science exposure on healthcare projects.
Benefits
Compensation and benefits
- Base salary: USD 170,000 to 205,000 per year
- Equity
- Performance bonus
Location and work model
- Remote (US)
- Full-time
Benefits mentioned
- Compensation and benefits
- - Base salary: USD 170,000 to 205,000 per year
- - Equity
- - Performance bonus
- Location and work model
- - Remote (US)
- - Full-time
Requirements mentioned
- What we're looking for
- - About 5 to 15 years of hands-on engineering, including running real production systems from design through operation rather than only prototypes.
- - Skill at planning complete system designs and communicating them through clear diagrams.
- - Background in healthcare or life sciences, ideally touching insurance, patient-facing services, clinical workflows or regulatory compliance.
- - A record of leading technical work for external customers across the sales process, scoping and implementation.
- - Experience delivering customer-facing software in a solutions engineering, solutions architecture, consulting or similar engineering role.
- - Strong command of Python and TypeScript, plus cloud platform experience.
- - Experience building data pipelines and machine learning workflows.
- - Polished communication and presence with executives as well as technical and non-technical audiences.
- - A founder-style mindset: high ownership, accountability for significant client outcomes and comfort making calls under pressure.
- Bonus points
- - Time spent in an early-stage or high-growth startup engineering environment, or as a founder or technical lead.
- - Hands-on work with LLM-powered applications or AI agents.
- - Formal machine learning or data science exposure on healthcare projects.
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