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Salary: USD 200,000 - 220,000 / annual
Acclinate is a venture-backed (post-Series A, backed by Cencora and Labcorp) digital health company using patented technology and 6+ years of proprietary community intelligence to empower underserved communities toward better health. The platform engages over 275K members and processes more than 100 million data points spanning behavioral and social determinants of health (SDOH). Acclinate helps leading pharma companies and healthcare providers access, engage, and mobilize underserved communities.
You will be a hands-on player-coach leading and growing the engineering team while shipping code directly. You will own the engineering function and continue scaling AVA into an enterprise-grade platform, balancing rapid innovation with strict healthcare compliance.
Key Responsibilities:
Architecture & Systems: Own the full-stack systems handling complex proprietary data, analytics pipelines, and LLM-powered features. Maintain and modernize the core technology stack, including GCP-native infrastructure (Cloud Run, BigQuery, Pub/Sub, Vertex AI), containerization, CI/CD, and Ruby on Rails. Lead exploration of new stacks to modernize frontend/backend. Own integrations across the data and business ecosystem (Metabase, HubSpot, etc.).
Data Confidence & Traceability: Ensure confidence in data pulled from multiple external platforms, predictive scoring models, and reporting. Trace metrics to their source, fix pipelines when upstream platforms change. Write SQL to answer customer questions and stand behind the numbers. Build and run integrations pulling from third-party APIs, normalize messy external data, and maintain reporting accuracy.
Quality & Delivery Standards: Own quality and delivery through small PRs, comprehensive tests, non-breaking migrations, structured logs and traces. Maintain strong security and compliance standards including data security, audit trails, HIPAA compliance, and SOC 2 readiness without introducing unnecessary friction. Modernize engineering processes through streamlined code reviews, automated testing, and production monitoring.
Product & Customer Partnership: Carry product judgment into code. Join customer and partner calls when needed. Turn thin specs into working software without waiting for extensive documentation. Push back when requests won't hold up in production. Serve as strategic partner to Product, Community, Marketing, Customer Success, and Commercial teams.
Team Building & Mentorship: Manage, mentor, and work alongside the existing engineering team. Build and scale the team intentionally, hiring engineers who excel in rapid prototyping, system architecture, and disciplined delivery. Onboard new engineers into the codebase and customer context.
AI-Native Development: Drive Claude Code, Cursor, or equivalent for most output. Treat AI tools as collaborators. Know when to prompt, when to code by hand, and how to critically evaluate AI-generated output. Write repo instructions and decision records that help AI agents produce correct code. Structure the codebase so AI agents can succeed. Review agent output with rigor. Help the team improve AI leverage through workflow refinement and best practices.
Requirements:
Core Leadership & Engineering Capability: Open to candidates with 8+ years of traditional engineering leadership who have recently pivoted to Claude-native thinking (proven by shipping); OR 5+ years in AI-native shops with deep knowledge; OR early-to-mid career (3–5 years) who is exceptionally clear at moving fast and shipping, having outpaced peers with more time.
Technical Depth: 5+ years building production systems, with at least one system taken from early design to customers. Experience with data pipelines, data security, and integrating modern AI tooling or LLM APIs into production applications. Strong cloud experience (GCP strongly preferred), including Cloud Run, BigQuery, Pub/Sub, Vertex AI, and CI/CD best practices. Full-stack capability across backend architecture, data infrastructure, APIs, integrations, and front-end experiences. Ruby on Rails and a typed client framework preferred; strong Rails plus React with willingness to learn Flutter also works. Data-model-first thinking: design tables and APIs before screens, explain tradeoffs.
AI Tooling Fluency: Daily use of AI coding agents on real codebases with clear understanding of failure modes. Ability to describe how to structure a repo so agents produce correct code and what to check before merging. Use AI to accelerate development, explore unfamiliar codebases, and improve productivity while maintaining high standards for code quality, testing, and ownership.
Communication & Mindset: Plain, direct written communication. Most coordination is asynchronous. Write PR descriptions, decision records, Slack updates, and strategy memos that non-engineers can follow. Comfort with small teams and real customer bases. Ambiguity is normal; shipping is expected. Systems thinking: reason about the whole system (data in, jobs, storage, API, client, result). Design for failure modes, not the happy path.
