Vice President of Engineering

Fully remote, we're hiring worldwide! Search Atlas: Frontier Agentic Systems at Scale $35M ARR | Bootstrapped Location: Remote from Anywhere Reporting to: CEO (Manick Bhan) The Mission: Scale the Engine from $35M to $100M ARR The Moment Search Atlas bootstrapped to $35M ARR by solving a problem most companies don't even attempt: autonomous marketing execution at enterprise scale. We've cracked the code on daily autonomous deployment. We've proven that AI can generate, review, and ship production code reliably. We've scaled agentic systems to orchestrate hundreds of integrated services. Now we're hitting the walls that only frontier companies face. The problems we're solving, perpetual agent coordination, cost-quality tradeoffs in LLM orchestration, distributed state management in autonomous systems, aren't taught in universities or discussed in most engineering organizations. You're the person who's stared into these kinds of problems before. We're not hiring a traditional VP. We're looking for a engineer who leads, in the mold of Staff+ ICs at Anthropic, OpenAI, or the infrastructure teams at hyperscalers. What You'll Actually Do: Daily Technical Reality (60% of your time) Whiteboard architecture for systems running autonomous agents continuously, where every architectural decision impacts token costs, execution latency, and quality; Review critical-path code in our agentic orchestration layer, not to micromanage, but because decisions here ripple to millions of requests; Solve distributed systems problems that arise when coordinating multiple LLM-based agents with bounded autonomy, managing context windows, and optimizing state persistence; Unblock engineers by doing hands-on technical work: refactoring a bottleneck, designing a new subsystem, debugging a production incident that requires deep systems thinking Make real-time tradeoff decisions: Cost vs. quality. Speed vs. reliability. Cheaper models vs. frontier models. You'll spend time analyzing token efficiency, model performance, and execution patterns. Code daily. Not ceremonial code reviews or architecture docs. Real code. You ship features, you optimize critical paths, you own the forensics when something breaks. Leadership & Scaling Reality (40% of your time) Lead 100+ engineers through hands-on mentorship, not management theater. Your team gets better by watching you solve a hard problem, not by sitting through standup meetings. Identify and develop the next generation of technical leaders. You know how to spot an operator in 15 minutes and distinguish them from someone who looks good in meetings. Set technical standards that elevate the entire organization. TDD, trunk-based development, aggressive deployment cadence, not as buzzwords, but as lived practice. Shape engineering culture around ownership: end-to-end responsibility, no project managers, no blame games. Engineers own what they build, all the way to production and monitoring. The Technical Context The Complexity You'll Face Multi-Agent Orchestration at Scale: Coordinating autonomous agents across dozens of decision points Managing context window constraints while maintaining quality Designing agent architecture where each component must be reliable (failure in one agent cascades) Balancing tool availability vs. token efficiency vs. execution speed LLM-Driven Code Generation in Production: ~70% of our production code is generated by agentic systems Continuous deployment of AI-generated changes (daily releases) Evaluation frameworks that catch quality degradation before it reaches production Prompt versioning, golden datasets, and automated testing for model changes Understanding when to use different models for different tasks, optimizing for cost and quality Long-Running Autonomous Systems: Agents that need to operate perpetually, learning from each execution State management across distributed agents without degradation Memory constraints in systems designed to run for days/weeks Error recovery and circuit breakers when things inevitably break Cost-Quality-Speed Trifecta: Managing token costs at scale (millions of LLM calls daily) Quality degradation when moving away from frontier models Latency requirements (sub-100ms critical path) Architectural decisions that compound costs: choosing the wrong model, over-contextualizing agents, or poor tool management can cost millions monthly Integration Complexity: Orchestrating dozens of microservices as "tools" available to agents Handling microservice reliability, failures, and inconsistent responses Designing APIs that agents can reliably interact with Managing the blast radius of changes across tightly coupled systems The Stack You'll Inherit Core Infrastructure: Backend: Python (Django, FastAPI), async processing at massive scale (Celery/RQ) Data Layer: PostgreSQL (transactional), ClickHouse (analytics), Elasticsearch, Redis Compute: Kubernetes, Docker, multi-region coordination Monitoring: Datadog, Sentry, Grafana (real-time visibility into agentic systems) AI/ML: Vector databases, LLM APIs (Anthropic, OpenAI, Google), prompt management, evaluation frameworks What Makes It Hard: Not a typical SaaS stack, this is a systems company built for continuous autonomous execution Scale you can't think your way out of; you have to engineer solutions Tight coupling between business logic, AI systems, and infrastructure (changes in one layer affect all) Real-time requirements competing with cost constraints Who We're Actually Looking For The Profile You have 10+ years of shipping production code, with 3+ years of hands-on technical leadership scaling engineering organizations from 30-100+ people. But here's the catch: you didn't stop coding when you became a leader. You're the person who: Has operated at frontier: You've worked at Anthropic, OpenAI, a major hyperscaler, or a well-funded startup solving problems at the edge of what's technically possible Understands agentic systems (not as theory, but from shipping production code): Multi-agent orchestration, LLM integration, reliability under uncertainty, cost optimization in AI systems Has hit scaling walls you had to engineer your way out of: Token management, context window constraints, perpetual system design, distributed state, cost-quality tradeoffs Lives and breathes shipping: You measure cycle time, deployment frequency, MTTR. You see waste and immediately think "how do we eliminate this?" Attracts elite engineers: The top 1% of SF engineers want to work for you because they know you'll make them better. You have a reputation for technical rigor and mentorship through action. Combines depth with breadth: You're comfortable reviewing code at any layer - infrastructure, backend, frontend, ML ops. You know when you're out of your depth and have no ego about learning. Thinks like a founder: You understand unit economics, customer impact, and how engineering decisions compound. You're not optimizing for resume-building; you're optimizing for what matters. What You're Not A manager who delegates all code to direct reports Someone who measures success in headcount or org charts A process engineer ("let's add more meetings to align on decisions") Someone uncomfortable with ambiguity or unclear priorities A politics player (we don't have room for that) What Success Looks Like (Year 1 & Beyond) In the First 90 Days You've mapped the technical landscape: where the bottlenecks are, which systems are fragile, where we're overspending on compute or tokens You've identified 3-5 technical leaders on the team and are working with them directly You've shipped at least one meaningful piece of code (not ceremonial—something that improves the system) You've had multiple technical sparring sessions with Manick where you've pushed back on decisions and earned trust through depth In Year 1 Engineering velocity has noticeably increased: faster cycle times, more confident deployments, fewer surprise production incidents The quality bar has risen: evals are more rigorous, code reviews are sharper, but you've done this through mentorship, not rules You've solved at least one major technical problem that was blocking the organization: a scaling bottleneck, a reliability issue, a cost problem You've hired 5-10 exceptional engineers who all say "I came to work for [you], not the company" You've reduced technical debt in critical systems while shipping features The organization runs tighter, faster, with less friction In Years 2-3 We've scaled to $75-100M ARR with engineering quality improving, not degrading You've developed 3-4 technical leaders who could step into your role The agentic systems we've pioneered are industry-leading—harder to copy than our product features We've solved the hard technical problems that are currently blocking us, and we're now solving the next level of hard problems You own a significant piece of equity in a profitable, high-growth company The Role in Numbers Scale You'll Touch: 100+ engineers across backend, infrastructure, ML ops, and frontend Millions of autonomous executions daily Sub-100ms latency requirements on critical path Multiple terabytes of data processed and stored 99.99% uptime requirements in production Technical Decisions You'll Make: Architecture for distributed agents (how they coordinate, share state, handle failures) LLM strategy: which models for which use cases, how to optimize cost while maintaining quality Infrastructure: how to scale Kubernetes, optimize database performance, manage cloud costs Code generation reliability: how to make AI-generated code safe for production Team growth: hiring, mentorship, and technical track allocation The Partnership How You'll Work Rhythm: Weekly releases, continuous measurement, end-to-end ownership Culture: Radical candor, high expectations, no bureaucracy, shipping obsession Access: Direct to founders, executive team, and decision-makers (no middle management filtering) The Interview Process This isn't a typical hiring funnel, we're looking for mutual fit. Stage 1 — Recruiter Screen: A technical call, we'll go over your engineering set-up, how you leverage agentic systems. Stage 2 — Technical Assessment: A deep dive into your technical skills, we're looking at how you approach problems and your capability to extrapolate at scale. Stage 3 — Technical brief: A technical brief with our SVP Tomás Lopes. Stage 4 — Founder meeting: A meeting with our Founder Manick Bhan. He will be gauging your technical depth. One More Thing This role is genuinely hard. The problems we're solving are at the frontier of what's technically possible with agentic systems and AI at scale. You'll hit moments where you're not sure how to proceed. You'll make decisions with incomplete information. You'll have to do technical work that stretches you. That's exactly why we need you. If you've stared into problems this hard before, if you've shipped systems at this scale, if you've built teams that get better by watching you work and if you're ready to do it again, let's talk. About Search Atlas We build autonomous marketing software for Fortune 500s and high-growth startups: OTTO SEO - Fully autonomous technical optimization Content Genius - Semantic AI content generation Site Explorer - Real-time search intelligence BrandVault - AI-driven knowledge graphs GBP Galactic - Local SEO automation Smart Ads - Autonomous PPC management Recognition: Inc. 5000, Nevada's #1 Small Business Workplace, Great Place to Work Certified $35M ARR. Bootstrapped. Profitable. Growing fast. No cover letters. No generic applications. Just evidence of scientific craft. Search Atlas is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all team members. Apply To This Job