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About This Role
Software Engineer — AI\-Native Full Stack
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### Bolo.ai
Bay Area (Hybrid) \| Salt Lake City Area (Remote) \| Full\-Time Senior Engineer
The Role Has Changed
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Three person engineering teams are building what used to take thirty. Not by working harder, but by working differently. The engineers shipping at this pace don't write code. They write specs precise enough that agents implement them correctly. They build harnesses. CI gates, structural tests, linting rules, and architectural enforcement that mechanically prevent entire classes of agent mistakes. They design validation systems where agents write the tests and humans verify that features actually work from the user's perspective.
The code is a generated artifact. The spec, the harness, and the validation infrastructure are what engineers maintain.
This is how we work at Bolo.ai. We're hiring engineers who already work this way, or who have the depth to start.
The Company
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Bolo.ai builds generative AI systems for the energy industry, making daily work faster, safer, and better for heavy industry workers. We have Fortune 500 contracts, production deployments, and growing enterprise demand. We're scaling.
Energy adds real constraints. Regulatory compliance, data residency, operational technology integration, deployment across cloud and on\-premises infrastructure. These constraints make the architecture harder and the work more interesting.
The Work
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You'll spend your time on four things:
Specifications. You write behavioral specs, architectural constraints, and feature requirements that agents implement against. When agent output misses the mark, you tighten the spec. Not by adding more words, but by being more precise about what "correct" means. This requires understanding the system deeply enough to define its behavior at every layer.
Harness. You build and maintain the infrastructure that keeps agents producing reliable code. Structural tests that enforce architectural boundaries. Linting rules where every failure message teaches the agent what went wrong. CI gates that reject drift. Structured knowledge bases agents can navigate. The principle: every class of agent mistake gets a mechanical fix so it never recurs.
Validation. Agents write the code. Agents write the tests. You verify that features work from the user's perspective, under real deployment conditions, against edge cases that matter in production. You define scenarios and acceptance criteria. You build the end\-to\-end checks,
behavioral verification, and automation that make this trustworthy at scale. When something breaks, your job is diagnosing whether the failure is in the spec, the harness, or the agent's implementation, and fixing the right layer.
Architecture and operations. Our systems run across cloud providers and on\-premises environments. You design modular abstractions, clean interfaces where deployment targets don't leak into application logic. You own production systems used by energy companies in regulated environments where failures have real consequences. Reliability, observability, and graceful degradation matter here.
What Makes Someone Good at This
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7\+ years of engineering experience, applied at a higher altitude. You need years of building and debugging production systems. Not because you'll write every line, but because you can't design a harness that catches real failures, write a spec that anticipates edge cases, or diagnose a broken feature across the full stack without that foundation. The depth serves the abstraction.
Systems thinking over code fluency. How components interact. Where failures cascade. What breaks when requirements change. What to anticipate before it happens. This is what agents are worst at and what matters most.
An agent\-driven workflow. You already direct AI agents (Claude Code, Codex, Cursor, or similar) to handle implementation while you focus on architecture, specification, and validation. Or you have the engineering judgment to make that transition and the motivation to do it now.
Experience building the infrastructure around agents. CI enforcement, scenario\-based testing, documentation systems agents can consume, structured knowledge bases — you've built some of this, or you have specific ideas about how and why.
Comfort making decisions with incomplete information. Startup. Requirements shift. The right approach isn't always obvious. You move forward, and you know when to ask versus when to make a call.
Direct communication. You give and receive honest feedback. You can disagree with a decision, say so clearly, and still commit to the outcome. We care about getting it right more than being right.
Enthusiasm for a field that reinvents itself quarterly. Tools change. Workflows get replaced. Best practices from three months ago become obsolete. You're energized by that. You see this as the most interesting period in the history of software.
About Us
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Small, senior\-leaning engineering team. Real ownership, direct impact, no layers between you and the work. We expect a lot from each other and give each other the room to deliver.
Sustainable pace over heroic sprints.
Bay Area (hybrid) or Salt Lake City area (remote). No visa sponsorship.
What We Offer
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Bolo AI is headquartered in Palo Alto, backed by True Ventures, Benchstrength, Accomplice, J Ventures, and Beat Ventures.
- Competitive compensation with equity so you share in what we build together.
- Hybrid flexibility — in\-person collaboration in Palo Alto with room to work how you're most productive.
- Early\-stage ownership — join at a stage where your decisions shape the product, the architecture, and the engineering culture.
- Generous PTO and flexible working hours.
Hiring Process
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We evaluate how you work in an AI\-native workflow. AI tool usage is expected, not just permitted. We're looking at engineering judgment. Can you write specs agents execute well against, build systems that catch real failures, and reason about problems across the full stack.
We'll be straightforward about our process, give you real information to evaluate us, and give you feedback regardless of outcome.
*If this sounds like what you're already building toward, we'd like to talk.*
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Bolo AI, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Bolo AI AI Hiring
Bolo AI has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Francisco Bay Area, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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