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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.

Vice President of Engineering

Benefits

Job Description