About Flowmingo
Flowmingo is a Y Combinator-backed startup that is building an AI-powered interviewing platform, so companies can interview 100% of applicants, not just the top 5–10 who “look good on paper.” We’re starting in the Global South with a free, turn-based AI interview for recruiters—unlocking 10x more screening at zero cost—while monetizing via optional candidate services and a corporate opt-out for candidate offers. We’re building a high-volume product with a clear long-term moat: a candidate + company network effect that gets stronger with every interview completed.
The Role
We’re hiring a CTO to own technology strategy and execution end-to-end: product engineering, AI architecture, reliability, security, and cost efficiency. This is a hands-on leadership role: you’ll set the technical direction, ship core capabilities, and build an engineering culture that can scale to millions of interviews.
- Delightful and fast on low-to-high-end devices
- Extremely cost-efficient (client-side STT/TTS where possible, graceful fallbacks, fine-tuned LLM)
- Trustworthy and safe (privacy, compliance, bias mitigation, auditability)
- Built to scale globally (latency, infra, monitoring, incident response)
What You’ll Own (Responsibilities)
- Product & Engineering Leadership
- Define and execute the technical roadmap aligned with growth, retention, and unit economics.
- Lead architecture decisions across web, backend, AI services, data pipeline, and analytics.
- Establish engineering standards: code quality, testing, CI/CD, release discipline, and on-call.
- Partner tightly with CEO/product/growth to ship, learn, and iterate quickly.
AI, LLMs, Fine-Tuning & Evaluation Systems
- Own the end-to-end intelligence of the interview experience: question generation, rubric scoring, evaluation reports, and recruiter playback.
- Define the LLM strategy, including model selection, routing, caching, and fallbacks based on device/network constraints.
- Design and ship a fine-tuning pipeline: data collection, labeling, versioning, training runs, deployment, monitoring, and rollback.
- Improve AI intelligence through an iterative loop: rubrics → training data → fine-tune → eval → production → learn.
- Build evaluation systems that recruiters trust: explainability (why a score), confidence, and evidence.
- Strengthen multilingual capability and role-specific interviewing packs via lightweight fine-tunes and/or RAG.
- Implement model monitoring: regression suites, drift detection, and A/B testing of model/prompt changes.
Client-Side / Low-Cost Architecture
- Drive the “low-cost foundation” strategy: push STT/TTS to the browser where feasible; ensure graceful degradation and server-side fallbacks when needed.
- Own performance optimization for mobile and low bandwidth environments.
- Reduce cost-per-interview while improving perceived quality and reliability.
- Make pragmatic tradeoffs across cost, speed, and accuracy—without compromising the user experience.
Platform Scale, Reliability, and Security
- Ensure secure handling of video, transcripts, and CV data (access control, encryption, retention policies).
- Build monitoring and alerting (latency, error rates, conversion funnels, model failures).
- Establish operational excellence: incident response, postmortems, SLOs/SLAs as appropriate.
- Implement abuse prevention and platform integrity (spam, adversarial inputs, deepfake concerns, misuse patterns).
- Build privacy-forward systems suitable for high-volume, high-sensitivity hiring workflows.
Team Building & Culture
- Hire and lead the engineering org (full-stack, AI/ML, infra, QA).
- Create a culture of speed + rigor: rapid iteration with strong fundamentals.
- Build clear ownership, strong technical documentation, and a healthy engineering cadence.
Ideal Candidate Profile (Skills & Experience)
You might be a fit if you’ve done several of these:
- Built and scaled a product from early stage to meaningful volume (consumer, B2B SaaS, or marketplaces).
- Strong system design: async workflows, video pipelines, data-heavy products, and high availability systems.
- Deep experience with LLMs in production: prompt design, structured outputs, tool/function calling, retrieval (RAG), caching, and latency/cost optimization.
- Demonstrated experience with fine-tuning/model adaptation (SFT, preference tuning like DPO/RLHF-style approaches), plus evaluation-driven iteration.
- Strong LLM evaluation & monitoring practice: offline/online evals, hallucination/robustness tests, regression suites, A/B testing, drift detection, model observability.
- Security and privacy mindset with experience handling sensitive user data.
- Comfort optimizing for emerging markets constraints: low bandwidth, low-to-mid devices, multi-language UX.
- Hands-on leadership: you can architect, code, review, mentor, and hire.
Tech Stack (Flexible)
We’re open on exact choices, but the role will likely involve:
- Web app + backend APIs, storage, queues, observability
- AI orchestration + evaluation pipelines
- Client-side ML (browser STT/TTS) + fallbacks
- If you have a strong, opinionated stack that fits the mission (fast, cheap, global), we want to hear it.
Why This Role Is Special
- Massive mission: unlock opportunity for overlooked talent at global scale.
- True product wedge: free tier that makes adoption easy, with clear monetization paths.
- Hard, defensible tech: high-volume, cost-optimized AI interviewing with network effects.
- Real ownership: you’ll shape the foundation, team, and technical moat.
Location / Work Style
Ho Chi Minh City, Vietnam
Compensation
Competitive early-stage package: salary + meaningful equity, aligned with impact and ownership.
