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• Design AI-based systems that: • Explore UI, APIs, workflows, and edge cases without pre-defined scripts • Generate and execute tests dynamically from product behavior • Discover bugs through exploration, not just assertions • Move beyond static automation into self-directed testing systems • Build QA agents that: • Continuously test features as development agents build them • Provide structured, actionable feedback in real time • Loop with development agents until quality thresholds are met • Validate quality across: • Web UI • Backend services • APIs • Data integrity • End-to-end user journeys • Ensure the system works as a cohesive product, not just isolated components • Automatically identify: • Functional defects • Workflow breaks • Regression issues • Performance and reliability risks • Ensure issues are: • Reproducible • Verified • Clearly explainable to humans • Build systems that: • Run continuously • Adapt as the product evolves • Increase coverage automatically over time • Define what “release-ready” means • Block releases when quality is insufficient — with evidence • Raise risks early, clearly, and decisively • Daytime hours with 4–8 hours of overlap to US Pacific, with flexibility for critical releases or audits.

Role Description This is not a transitional or maintenance QA position. We are building agentic software development, and we need QA that operates at the same speed, intelligence, and autonomy as development. You will design and own an AI-driven QA system that: - Tests complex SaaS products end-to-end - Discovers bugs autonomously (without hand-written test cases) - Simulates how a new human QA engineer would deeply explore the product - Runs continuously (ideally 24×7) - Integrates directly into agentic development loops If your idea of QA is mostly writing Selenium scripts from requirements, this role is not for you. The Mission Build and operate an autonomous QA engine that: - Thinks like a human QA engineer - Acts like an AI system - Never sleeps - Never lets defects escape QA here is not a gate at the end — it is a real-time adversary to development agents until quality is proven. What You Will Own (End-to-End) - Autonomous, AI-Driven QA - Design AI-based systems that: - Explore UI, APIs, workflows, and edge cases without pre-defined scripts - Generate and execute tests dynamically from product behavior - Discover bugs through exploration, not just assertions - Move beyond static automation into self-directed testing systems - Agentic QA × Agentic Development Integration - Build QA agents that: - Continuously test features as development agents build them - Provide structured, actionable feedback in real time - Loop with development agents until quality thresholds are met - Own the QA feedback loop as a first-class system, not a manual process - Full-Stack Quality Coverage - Validate quality across: - Web UI - Backend services - APIs - Data integrity - End-to-end user journeys - Ensure the system works as a cohesive product, not just isolated components - Intelligent Defect Discovery & Verification - Automatically identify: - Functional defects - Workflow breaks - Regression issues - Performance and reliability risks - Ensure issues are: - Reproducible - Verified - Clearly explainable to humans - Continuous, Always-On QA - QA does not stop at “test execution” - Build systems that: - Run continuously - Adapt as the product evolves - Increase coverage automatically over time - Quality Ownership - Define what “release-ready” means - Block releases when quality is insufficient — with evidence - Raise risks early, clearly, and decisively What Success Looks Like - Bugs are found before humans see them - QA coverage grows without linear human effort - Development speed increases because QA is faster - QA agents actively challenge dev agents - Releases feel boring — because nothing breaks Schedule Daytime hours with 4–8 hours of overlap to US Pacific, with flexibility for critical releases or audits. Who You Are You think of QA as a system design problem, not a checklist. You’re excited by: - Autonomous systems - AI-driven exploration - Replacing manual effort with intelligence - Designing feedback loops, not just test suites You’re uncomfortable with: - Manually writing hundreds of brittle test cases - QA that reacts instead of anticipates - Automation that breaks every sprint Required Experience - 3+ years in QA (manual + automation) — foundation matters - Strong understanding of: - SDLC - Test strategies - Failure modes in complex SaaS systems - Hands-on experience with: - UI automation (Playwright / Cypress / Selenium) - API testing - Demonstrated use of AI in QA beyond basic test generation - Autonomous testing - AI-assisted exploratory testing - Intelligent test orchestration - Ability to explain how AI systems decide what to test next Strongly Preferred - Experience designing QA frameworks or platforms - Experience integrating QA into CI/CD at a system level - Performance / reliability testing - Healthcare or regulated systems experience - Prior work where QA influenced architecture decision Benefits - Remote-first - Equipment stipend - Local holidays (India) - Learning budget (QA, security, cloud, AI tools)

• Conduct in-depth healthcare and compliance research to understand regulatory landscape, market dynamics, and industry trends. • Analyze healthcare data, operational metrics, and market information to identify patterns, gaps, and opportunities. • Build reports and visualizations to synthesize research findings and communicate insights to leadership and cross-functional teams. • Research compliance requirements, healthcare regulations, privacy requirements, and industry best practices relevant to mental health care delivery. • Conduct market analysis, competitive research, and customer research to inform strategic decision-making. • Synthesize complex healthcare information into clear briefs, white papers, and strategic recommendations for leadership. • Support cross-functional teams with data analysis, research support, and evidence-based insights for strategic initiatives.

We're building a future where every mental health provider can deliver the full spectrum of care, including advanced interventions such as transcranial magnetic stimulation (TMS), SPRAVATO®, and others without the complexity of going it alone. We partner with mental health practices to bring these life-changing treatments to the people who need them most. Our tech-enabled platform embeds the end-to-end infrastructure, technology, and operations directly into existing practices so providers can expand their care, and patients can finally access the treatments they deserve. Everbright is currently partnered with 75+ providers across seven markets and backed by $7M in early-stage venture capital funding. About the Role We're seeking a Product Analyst to join our India-based team supporting US operations. This is a research and analysis-focused role ideal for someone with strong analytical skills, healthcare knowledge, and expertise in compliance and regulatory research. You'll conduct in depth healthcare research, analyze market trends, and investigate compliance landscapes to generate insights that inform product strategy and operations. If you have experience in healthcare research, regulatory analysis, or healthcare compliance, this is your opportunity to make a direct impact. You'll work with the product team to conduct market research, analyze healthcare trends, research compliance requirements, and synthesize complex information into actionable insights. The ideal candidate is structured, curious, and research driven a detail oriented operator who thrives on deep analysis, understands healthcare complexity, and excels at turning ambiguity into clear, evidence-based recommendations.

• Support business operations across functions including hiring, IT, and core operations. • Conduct research and data analysis to inform strategic and operational decisions. • Build dashboards and reports to track KPIs, identify trends, and support leadership reviews. • Manage cross-functional projects - tracking timelines, deliverables, and dependencies. • Document and optimize processes, creating templates, SOPs, and internal playbooks. • Assist in vendor coordination, tool implementation, and workflow improvements. • Contribute to strategic initiatives such as cost optimization, capacity planning, and hiring operations.

• Delivery Outcomes You will oversee delivery for all products — scope management, release cadence, quality controls, and stakeholder alignment. If something is delayed, it's your responsibility. If a product ships smoothly, you can claim that success. You'll shield engineers from scope changes and give the CEO predictable delivery rather than last-minute heroics. • Agentic SDLC & AI Governance (The Differentiator) This is the core of what makes this role unique. You will own the design and execution of our agentic software development lifecycle: - Human-agent workflow design: Define how AI agents participate in coding, testing, code review, and documentation — and where human engineers must intervene. - Maker-checker patterns: Build quality gates that catch AI sloppiness. Every AI-generated artifact needs a human verification step calibrated to the risk level — a UI tweak needs a different checkpoint than a database migration. - Agent orchestration: Determine which agents we use, how they're configured, what guardrails they operate within, and how engineers supervise their output. - AI tool governance: Define approved tools, IP protection policies, and ensure AI accelerates development without introducing risk — especially given the sensitivity of clinical/PHI data. - Continuous refinement: This model is new. You'll measure what's working, what's failing, and iterate. The playbook doesn't exist yet — you'll write it. • Engineering Team You will directly manage the engineering team — hiring, performance, coaching, feedback, conflict resolution, and retention. The team is small and high-leverage; every person matters disproportionately. You'll set the culture and performance bar. Difficult conversations happen early. Engineers will want to work with you because you are fair, direct, and invested in their growth. • System Architecture You own the architecture across the full stack: web applications, APIs, infrastructure, and AI integrations. You'll make trade-off calls — speed vs. rigor, refactor vs. ship, infrastructure vs. features. You should be capable of reviewing code, debugging production issues, and challenging architectural decisions with substance. In a clinical data environment, architectural choices carry compliance and safety implications — you'll factor those in. • CI/CD and Release Engineering You will build the release pipeline — CI/CD, environments, quality checkpoints, deployment automation. Chaotic releases end. You'll create a system that lets the team (and their agents) ship confidently and on a predictable cadence. • Security & Compliance Posture You own engineering security: access controls, secrets management, audit trails, and SDLC security. Healthcare data — especially mental health data — demands this. You'll also ensure AI-generated code and agent workflows meet audit and compliance requirements. Enforce rigor without bureaucracy. • Hiring & Team Building You will build the engineering team — define roles, maintain hiring standards, run technical interviews, and make hiring calls. You're building the organization that takes the company from startup to scale. Given our agentic model, you'll also need to think differently about team composition: fewer engineers, higher caliber, optimized for agent supervision rather than raw code output. • Your First 90 Days **Week 1-2:** Immerse yourself. Meet each engineer individually. Understand every product, deployment, and pain point. Map the current human-agent workflows — what's working, what's brittle. Identify delivery risks and the single biggest bottleneck. Build trust through listening, not announcements. **Month 1:** Establish a regular delivery cadence. Define the release process and quality standards. Create communication rhythms (standups, retros, planning). Audit the current agentic workflows — identify where AI output lacks sufficient human review. Begin surfacing risks early and reliably, relieving the CEO from delivery oversight. **Month 2-3:** Standardize CI/CD across all products. Implement maker-checker quality gates for AI-generated code. Design the AI governance framework — approved tools, IP protection, PHI safeguards for agent workflows. Initiate architecture assessment with a clear roadmap (not a rewrite). Begin hiring to fill gaps. Build the engineering runbook. Establish feedback and coaching routines. **Ongoing:** Own engineering completely. Ship reliably. Refine the agentic SDLC continuously. Grow the team. Raise the performance bar. Make the CEO confident that engineering is in expert hands.

Role Description We’re looking for a Business Analyst – Business Operations Enablement to join our India-based team supporting operations in the US. This is a generalist role ideal for someone with a consulting or analytical background who thrives on variety - comfortable moving between: - Research - Data analysis - Project management - Hiring support - IT coordination - Operational improvement initiatives You’ll work closely with cross-functional teams to solve high-priority business challenges, streamline processes, and build scalable systems that enable Everbright’s growth. The ideal candidate is structured, curious, and execution-driven—a hands-on operator who enjoys turning ambiguity into clarity and ideas into impact. Responsibilities - Support business operations across functions including hiring, IT, and core operations. - Conduct research and data analysis to inform strategic and operational decisions. - Build dashboards and reports to track KPIs, identify trends, and support leadership reviews. - Manage cross-functional projects - tracking timelines, deliverables, and dependencies. - Document and optimize processes, creating templates, SOPs, and internal playbooks. - Assist in vendor coordination, tool implementation, and workflow improvements. - Contribute to strategic initiatives such as cost optimization, capacity planning, and hiring operations. Qualifications - 1 - 3 years of experience in consulting, business operations, analytics, or strategy. - Proven ability to analyze data, identify insights, and drive business recommendations. - Strong skills in Excel/PowerPoint and comfort with data visualization or reporting tools. - Excellent written and verbal communication skills with cross-functional partners. - Highly organized with strong attention to detail and the ability to manage multiple projects. - Comfortable working independently in a fast-paced, evolving environment. Requirements - Business operations and cross-functional project management. - Research, analysis, and reporting. - Process improvement and documentation. - Strong communication and collaboration skills. - Analytical problem-solving using Excel or BI tools. - Ability to work across time zones and distributed teams. Preferred Skills - Consulting or startup experience. - Healthcare or healthcare services exposure. - Experience with operational or financial modelling. - Familiarity with HR, IT, or vendor management processes. - Comfort working in early-stage, high-growth environments. Benefits You’ll be joining at the ground floor of a mission-driven venture, working closely with the founding team to shape healthcare delivery in India. This role offers a unique mix of strategic exposure and hands-on execution, setting you up for accelerated growth in strategy, operations, and leadership.

Role Title: Head of Engineering Location: Remote across India (~6 hours overlap with US Pacific time) Type: Full-time Reports to: Founder-CEO Compensation: Well above market for Indian startups at this level. We pay for the caliber we're hiring. About the Company Zenara Health builds GenAI-powered clinical decision support and workflow tools for mental health clinics. We integrate AI-driven platforms with professional clinical care to offer personalized and effective mental health solutions — from AI-enhanced evaluations to care coordination — creating a seamless digital experience for both patients and providers. We are an AI-native organization. That's not a marketing label. Our engineering model is fundamentally built around AI agents participating in the software development lifecycle. We are a startup, not a department. About the Role Pay close attention here. If you are an engineering manager who primarily conducts standups and writes status reports, this role may not suit you. We are transitioning from the product stage to the commercial stage — multiple products, real customers, sensitive clinical data. Our engineers are already delivering, and now we need cohesive engineering leadership to transform exceptional individual contributions into collective organizational success. What makes this role different from every other Head of Engineering posting: Our engineering team is small by design. A handful of high-caliber engineers orchestrate AI agents that handle significant portions of the SDLC — from code generation to testing to documentation. Your job is not to manage 20 engineers writing code. Your job is to design, implement, and continuously refine the human-agent engineering model — including maker-checker workflows, quality gates for AI-generated output, and escalation protocols that ensure AI speed doesn't come at the cost of AI sloppiness. This is a relatively new way of working. Very few people have deep experience running agentic SDLC at scale. We're not looking for someone who's done this exact job before — we're looking for someone with the raw intellectual horsepower to figure it out. High learning velocity, first-principles thinking, and comfort with ambiguity matter more than years on a resume. You will report directly to the founder-CEO, take ownership of results, and shape the engineering organization. This is the technical co-leader seat — ultimately becoming the person the founder relies on to own all of engineering. What You Will Own (Everything) 1. Delivery Outcomes You will oversee delivery for all products — scope management, release cadence, quality controls, and stakeholder alignment. If something is delayed, it's your responsibility. If a product ships smoothly, you can claim that success. You'll shield engineers from scope changes and give the CEO predictable delivery rather than last-minute heroics. 2. Agentic SDLC & AI Governance (The Differentiator) This is the core of what makes this role unique. You will own the design and execution of our agentic software development lifecycle: - Human-agent workflow design: Define how AI agents participate in coding, testing, code review, and documentation — and where human engineers must intervene. - Maker-checker patterns: Build quality gates that catch AI sloppiness. Every AI-generated artifact needs a human verification step calibrated to the risk level — a UI tweak needs a different checkpoint than a database migration. - Agent orchestration: Determine which agents we use, how they're configured, what guardrails they operate within, and how engineers supervise their output. - AI tool governance: Define approved tools, IP protection policies, and ensure AI accelerates development without introducing risk — especially given the sensitivity of clinical/PHI data. - Continuous refinement: This model is new. You'll measure what's working, what's failing, and iterate. The playbook doesn't exist yet — you'll write it. If you don't have a strong, opinionated perspective on how AI agents should participate in the engineering process — beyond "we use Copilot" — this role is not for you. 3. Engineering Team You will directly manage the engineering team — hiring, performance, coaching, feedback, conflict resolution, and retention. The team is small and high-leverage; every person matters disproportionately. You'll set the culture and performance bar. Difficult conversations happen early. Engineers will want to work with you because you are fair, direct, and invested in their growth. 4. System Architecture You own the architecture across the full stack: web applications, APIs, infrastructure, and AI integrations. You'll make trade-off calls — speed vs. rigor, refactor vs. ship, infrastructure vs. features. You should be capable of reviewing code, debugging production issues, and challenging architectural decisions with substance. In a clinical data environment, architectural choices carry compliance and safety implications — you'll factor those in. 5. CI/CD and Release Engineering You will build the release pipeline — CI/CD, environments, quality checkpoints, deployment automation. Chaotic releases end. You'll create a system that lets the team (and their agents) ship confidently and on a predictable cadence. 6. Security & Compliance Posture You own engineering security: access controls, secrets management, audit trails, and SDLC security. Healthcare data — especially mental health data — demands this. You'll also ensure AI-generated code and agent workflows meet audit and compliance requirements. Enforce rigor without bureaucracy. 7. Hiring & Team Building You will build the engineering team — define roles, maintain hiring standards, run technical interviews, and make hiring calls. You're building the organization that takes the company from startup to scale. Given our agentic model, you'll also need to think differently about team composition: fewer engineers, higher caliber, optimized for agent supervision rather than raw code output. Your First 90 Days Week 1-2: Immerse yourself. Meet each engineer individually. Understand every product, deployment, and pain point. Map the current human-agent workflows — what's working, what's brittle. Identify delivery risks and the single biggest bottleneck. Build trust through listening, not announcements. Month 1: Establish a regular delivery cadence. Define the release process and quality standards. Create communication rhythms (standups, retros, planning). Audit the current agentic workflows — identify where AI output lacks sufficient human review. Begin surfacing risks early and reliably, relieving the CEO from delivery oversight. Month 2-3: Standardize CI/CD across all products. Implement maker-checker quality gates for AI-generated code. Design the AI governance framework — approved tools, IP protection, PHI safeguards for agent workflows. Initiate architecture assessment with a clear roadmap (not a rewrite). Begin hiring to fill gaps. Build the engineering runbook. Establish feedback and coaching routines. Ongoing: Own engineering completely. Ship reliably. Refine the agentic SDLC continuously. Grow the team. Raise the performance bar. Make the CEO confident that engineering is in expert hands. What Success Looks Like - Engineering consistently delivers — the CEO no longer chases delivery updates. - The agentic SDLC is operational: agents produce, humans verify, AND quality holds. The maker-checker model is documented, measured, and improving. - Release cadence is predictable and accelerating; quality standards are enforced. - Engineers have clear ownership, regular coaching, and unambiguous expectations. - Architectural decisions are documented, reasoned, and account for clinical data sensitivity. - Security and compliance posture is strong — including for AI-generated code and agent workflows. - The hiring pipeline is active — you're building a team optimized for the human-agent model. - AI tools and agents operate within a clear governance framework — speed without risk. - The engineering organization is healthier, faster, and more reliable than when you arrived.

• Own UI/UX design for Zenara's assessment product, care/practice product, and platform layer. • Rapidly explore, iterate, and validate design ideas using AI tools and prompting. • Define UX hypotheses and evaluate feature adoption using quantitative signals. • Maintain and evolve the Zenara design system; define components, patterns, and design tokens. • Conduct user research and usability testing with real clinical users. • Design for healthcare users, ensuring speed, warmth, and clarity in workflows. • Ensure accessibility (WCAG compliance) and responsive layouts. • Collaborate closely with Product and Engineering on sprint deliverables. • Design AI-powered interaction patterns for clinical users.

Role Description If you're a designer who needs detailed specifications and pixel-perfect handoffs to start work, or who can't design at AI velocity, this role may not suit you. Our users are clinicians. They spend their days treating patients with complex psychiatric conditions. When they open Zenara, the interface needs to disappear — no learning curve, no cognitive load, just the information they need to make better clinical decisions. That's the design challenge: make powerful AI tools feel simple for people who didn't go to medical school to learn software. You'll be Zenara's dedicated designer, owning the entire visual and interaction layer of our products. You'll work with our Product lead (Chief of Staff) who sets strategic direction — your job is to bring it to life in AI-native design workflows and ship it with engineering. This means everything: our assessment product, our care/practice product, our platform layer, patient-facing interfaces, clinician dashboards, and AI-powered interaction patterns. You'll design for multiple user types — psychiatrists, practice managers, patients, and billing staff — each with different needs and technical comfort levels. This is product design with high clinical stakes and AI-native velocity. Poor UX doesn't just frustrate users — it can impact patient care. You'll need to balance simplicity with clinical depth, speed with accuracy, and innovation with familiarity. And you'll need to design in days, not weeks, leveraging AI tools to iterate rapidly while engineering refines in code. What You Will Own: - Product Design Across All Zenara Products: You will own UI/UX design for our assessment product, our care/practice product, and our platform layer. Create wireframes, prototypes, and high-fidelity designs. Design every screen, every interaction, every state. - AI-Native Design Workflows: You will rapidly explore, iterate, and validate design ideas using AI tools and prompting—prioritizing speed to usable product over pixel-perfect mockups. Produce "good enough to build" design direction quickly, knowing engineering will iterate and refine in code using AI-assisted development. - UX Experimentation and Data-Driven Iteration: You will define UX hypotheses and evaluate feature adoption using quantitative signals. Partner with product/engineering to design and interpret UX experiments (e.g., A/B tests, phased rollouts). - Design System and Component Library: You will maintain and evolve the Zenara design system. Define components, patterns, and design tokens. Ensure consistency across all products while allowing for product-specific needs. - User Research and Usability Testing: You will conduct user research and usability testing with real clinical users. Interview psychiatrists, observe workflows, identify pain points, and validate design decisions with actual user feedback. - Clinical Workflow Design: You will design for healthcare users: clinicians who need speed, patients who need warmth, admins who need clarity. Understand clinical workflows deeply enough to design interfaces that support them naturally. - Accessibility and Responsive Design: You will design for accessibility (WCAG compliance) and responsive layouts. Ensure interfaces work for users with disabilities and across different devices and screen sizes. - Collaboration with Product and Engineering: You will collaborate closely with Product (strategic direction) and Engineering (implementation) on sprint deliverables. Balance design ideals with engineering constraints. - AI Interaction Design: You will design AI-powered interaction patterns — chat interfaces, AI-generated content displays, recommendations, and automated workflows. Make AI features feel natural and trustworthy to clinical users. Your First 90 Days: - Week 1-2: Immerse yourself. Use Zenara's products. Conduct user interviews with clinical team members. Review existing designs and identify the biggest usability gaps and inconsistencies. Experiment with AI design tools in your workflow. - Month 1: Establish the design system foundation — core components, color palette, typography, spacing. Begin redesigning the highest-priority screens based on user feedback and strategic direction. - Month 2-3: Ship the first wave of design improvements. Conduct usability testing with clinical users. Run your first UX experiment (A/B test or phased rollout). Analyze adoption metrics and iterate based on data. - Ongoing: Continuously improve product design based on user feedback, usage data, and product evolution. Grow the design system. Establish design quality standards. Qualifications - Experience designing for healthcare or complex enterprise products. - Strong Figma user — think in components, variants, and design systems. - Conducted user research, usability testing, and UX experiments. - Strong English communication skills — Async-first, produces clear design rationale, user research findings, and design specifications. Requirements - 4-8 years as UI/UX Designer for B2B SaaS or healthcare products. - Expert Figma skills — you think in components, variants, and design systems. - Comfort using AI tools to accelerate design thinking. - Ability to define UX hypotheses and evaluate feature adoption using quantitative signals. - Portfolio showing healthcare or complex enterprise product design. - Experience designing for clinical workflows. - Understanding of accessibility standards (WCAG) and inclusive design principles. - Comfort working with product direction from a non-designer. Benefits - Fully remote work opportunities available across India. - Equipment allowance provided. - Culturally recognized local holidays (India). - Flexible paid time off. - Direct communication with product leadership. - The opportunity to establish AI-native design practices from scratch.