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About This Role
About the Role
Most growth teams don't lack ideas. They lack the infrastructure to test them fast enough to matter.
We're hiring an Agentic Operator whose core output is one thing: reducing the time between hypothesis and signal. You'll build the agent pipelines, orchestration layers, and automation systems that let our growth team run more experiments in parallel — and learn from them faster — than any traditionally-staffed team could.
This is a builder role. You'll treat AI agents as a programmable workforce, replacing human bottlenecks in the experimentation loop with systems that pre-validate, instrument, execute, and surface insights automatically. You won't be handed a playbook. You'll write it in code. You'll report to the Director of Demand Generation and work closely with channel owners, lifecycle, product, and sales to make the growth team's experiments faster, more rigorous, and more evidence-based.
The right person is operationally sharp with genuine growth instincts. You understand B2B SaaS funnels well enough to prioritize experiments intelligently and catch flawed hypotheses before they waste the team's time. You're technically fluent enough to build AI workflows, instrument experiments, and own the tracking systems the team depends on. And you bring the organizational discipline to manage a high-velocity experiment backlog without letting things slip through the cracks.
What You'll Build
Agent Pipelines for Concept Pre-Validation
Before human resources touch a test, your agents have already scored it — LLM-evaluated copy variants, synthetic audience testing, funnel drop-off analysis. Only the strongest hypotheses make it to execution.
An Automated Experiment Queue
You own the full loop: intake, prioritization, instrumentation, and readout — all accelerated via automation. Funnel drop-off doesn't trigger a Monday meeting; it triggers the next test.
An Experimentation Process
You own the full loop — backlog, sequencing, instrumentation, and readout — with the rigor to match. Every test ships with a falsifiable hypothesis, clean measurement setup, and defined success metric. Null results get documented, not buried. Channel owners, lifecycle, and PLG stay in sync so the team is always running the highest-leverage tests without conflicts or duplication.
Stack Orchestration
You wire together our existing tools — Amplitude, HubSpot, Clay, REO.dev and enrichment layers — so signals flow automatically into action. You integrate frontier LLMs (Claude, GPT) and workflow tools (n8n, Gumloop) into production-grade systems that improve over time.
Parallel Experimentation Infrastructure
Instead of sequential tests, you build the infrastructure to run experiments across channels simultaneously — compressing the learning cycle and multiplying the team's throughput without multiplying headcount.
What You'll Bring
Growth Fundamentals
You understand acquisition funnels, CRO, CAC/LTV, and hypothesis-driven testing deeply enough to know which experiments are worth building for. Instinct for what moves the needle isn't optional — it's the filter on everything you automate.
Agentic Build Skills
You've shipped multi-agent workflows in production, not just personal automation scripts. You're fluent in API-based orchestration, prompt engineering, and building feedback loops that self-improve. You've built internal tooling that other teams depended on.
Systems Thinking
You design for leverage. Before touching a tool, you ask: what's the bottleneck, what happens if this scales 10x, and where does the loop close? You build things that get smarter over time.
Zero-to-One Comfort
The playbook for this role doesn't exist yet. You've operated in environments where you had to define the process, not just follow it — and you preferred it that way. You want the opportunity to build, improve, and adapt a new experimentation motion.
Multi-stakeholder Management
Experience managing or coordinating a multi-stakeholder experimentation program — not just running your own tests, but helping a team run better ones.
You Might Be a Fit If…
- You can describe a specific experiment cadence you drove — e.g., "we went from 3 tests/month to 18" — and walk through exactly what you automated to get there
- Your first instinct when hitting a bottleneck is to build something, not escalate it
- You think about your tech stack second and your growth hypothesis first
CircleCI is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
About CircleCI
CircleCI is the world's largest continuous integration/continuous delivery (CI/CD) platform, and the hub where code moves from idea to delivery. As one of the most-used DevOps tools - processing more than 3 million jobs a day - CircleCI has unique access to data on how the most effective engineering teams work, and the tools to help software companies successfully leverage the power of AI into their commercial applications. Companies like Hinge, HuggingFace, and Samsung use us to improve engineering team productivity, release better products, and get to market faster.
Founded in 2011 and headquartered in downtown San Francisco with a global, remote workforce, CircleCI is venture-backed by Base10, Greenspring Associates, Eleven Prime, IVP, Sapphire Ventures, Top Tier Capital Partners, Baseline Ventures, Threshold
Ventures, Scale Venture Partners, Owl Rock Capital, Next Equity Partners, Heavybit, and Harrison Metal Capital.
CircleCI is an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
Salary Context
This $118K-$171K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CircleCI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($144K) sits 32% below the category median. Disclosed range: $118K to $171K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CircleCI AI Hiring
CircleCI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $171K - $171K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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