• Develop the Tailscale product, contributing to client code and backend services. • Bring a special focus on our mobile platforms, iOS and Android, while contributing to common code that supports macOS, Windows and other core client platforms.

• Help define the technical vision of the team/org, articulate how our data platform and architecture could evolve • Design, implement, and optimize our high-performance, scalable data serving platform that enables data querying and consumption across the organization and external facing data products • Design, implement, and optimize our high-performance, scalable data storage and transformation platform that enables both batch and stream processing with 100+ million updates per day on datasets > 100 billion rows • Build seamless integrations between Data Cloud and various relational and noSQL OLTP databases • Build batch and streaming data pipelines for core blockchain datasets widely used across the company • Deploy cloud infrastructure at scale with enterprise-grade reliability, implement and maintain infrastructure automation and self-service, and create robust CI/CD pipelines • Establish and maintain observability, security, and data governance solutions to ensure high quality, efficiency, and reliability of data pipelines

Role Description We are hiring a Platform Engineer on a contract basis to join the team building Howard's internal platform. You'll work alongside operators, leadership, and other engineers to ship the back-end systems, dashboards, and automations that run our business: - Practice management - Internal reporting - Team operations - The connective tissue between them We expect this role to be solutions-oriented and leverage the latest technology (Claude Code, etc.) to ship quickly. What You'll Do - Internal Platform Development - Build and maintain the back-end services that power Howard's internal applications - Develop dashboards and operational tools used by leadership, controllers, and the broader team - Ensure performance and reliability across large datasets (thousands of records, recurring jobs, scheduled syncs) - Contribute to a centralized internal platform that consolidates workflows across teams - Internal Systems Integration - Build middleware and API integrations between Howard's internal systems - Replace manual workarounds and spreadsheet bridges with automated, event-driven data flows - Maintain a clean source of truth across the systems our team uses every day - Automated Workflow Development - Engineer event-triggered automations that move data and actions across our internal stack without human intervention - Build the backend logic that ties operational events to downstream effects across other internal systems - Data Normalization & Schema Mapping - Build migration and sync logic that handles inconsistent data and maps it into a standardized internal schema - Own the data engineering layer that makes our internal platforms speak to each other cleanly - Custom Auth & Permissions - Build authentication and authorization layers for internal tools, with granular permissioning so sensitive data is cordoned off by department, role, or user level - Use HRIS data (e.g., Rippling) as a schema to drive internal permissions and team-level views Qualifications - Fluent using AI tools (Claude, Cursor, similar) as a core part of your workflow - Strong full-stack background with emphasis on backend and infrastructure - Deep experience with RESTful APIs and custom middleware development - Track record handling dirty data migrations and schema normalization - Comfortable with serverless and modern cloud architecture - Frontend competence (React/HTML/JS/CSS) sufficient to ship full features when needed Requirements - You should be able to read API docs and reason about the data model for the kinds of platforms we operate on: - Practice management and workflow tools (Notion, internal task systems) - HRIS and identity platforms (Rippling and similar) - Database and serverless platforms (Supabase, Postgres, similar) The Engineer Profile - Operational empathy: You can sit with a business process and translate it directly into automation logic - Ambiguity tolerance: Comfortable when the spec evolves based on what the business is learning - Lean middleware mindset: You build the right-sized pipe, not the cathedral Benefits - Real engineering problems with real business impact - Direct collaboration with operators and founders, no layers - A working environment where AI tooling is encouraged

• Deliver high-energy, synchronous remote lectures and "prompt-along" sessions covering ML pipelines, model deployment, and monitoring. • Translate high-level MLOps concepts into digestible insights for learners. • Guide students through self-paced exercises and live troubleshooting. • Provide real-time feedback during dedicated lab hours. • Ensure students can successfully articulate and execute model lifecycle management strategies by the end of the cohort.