Interested in this AI/ML Engineer role at Single Grain?
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About This Role
Reports to: Eric Siu, CEO
Type: Full-time, client-facing player-coach. Not a people-manager seat.
Clients: an assigned book of Single Grain SEO/AEO accounts
Location: Full-time, remote, with at least 4 hours of US time-zone overlap
Comp: $100,000–$150,000 USD / year
One-liner
Get Single Grain clients cited in AI answers and growing qualified organic pipeline, with agents doing most of the production.
About the role
Single Grain combines marketing services, software, and AI. Search is splitting between Google and answer engines (ChatGPT, Perplexity, Gemini, Google AI Overviews), and buyers now build their shortlist before they click. This seat owns SEO and AEO for an assigned set of client accounts: what to ship, why, and how we will know it worked.
You are the client's organic strategist and you stay hands-on. You set the roadmap, you build the agents that do the production, and you report citations and pipeline, not ranking screenshots.
You should be an advanced AI user. You will work inside Single Brain (our agent operating layer) and ClickFlow (our AI content-optimization platform), and build your own workflows in tools like Claude Code. Use AI for research, briefs, refreshes, internal linking, technical checks, and reporting, and check what it produces before anything ships.
What you will own
- Diagnose each client's search demand, answer-engine visibility, technical health, content system, and conversion path; set baselines and targets.
- Own the SEO/AEO roadmap for your accounts and decide what ships next.
- Track answer-engine citations: a fixed prompt set per client, run weekly across ChatGPT, Perplexity, Gemini, and Google AI Overviews, logging where the client and its competitors are cited.
- Build and maintain the agents that do production (research, briefs, refreshes, internal linking, technical checks, reporting) and get teammates using them.
- Keep brief-to-publish under five working days, with a human check on facts, brand, and technical accuracy.
- Lead client strategy calls with proactive recommendations; give writers, developers, and analysts clear acceptance criteria.
- Report monthly in terms a GM would use: citations, qualified organic traffic, and pipeline or revenue where attribution allows.
- Write runbooks so the system runs without you at the keyboard.
What success looks like
- By day 30: client diagnoses done; baselines set for citations and qualified organic pipeline; your AI citation tracker is live and at least one teammate runs it on an account they own.
- By day 60: an agent-built brief-to-publish pipeline is running on every account; the monthly citation + pipeline report is in use with clients.
- By day 90: brief-to-publish under five working days; at least one account up on answer-engine citations or qualified organic pipeline vs baseline; a runbook another person can follow. Failure = content volume going up with no citation or pipeline readback.
- By 6–12 months: citations and qualified organic pipeline up across your book; workflows do 70%+ of production; clients treat you as their go-to marketing strategist; you have designed yourself out of the daily queue.
Evidence we want to see
- 6+ years in SEO or organic growth, currently at Senior Manager, Associate Director, Director, or Head scope, with client-facing ownership.
- Shipped answer-engine or AI-for-SEO work we can inspect: the pages, the citations, and how you measured them.
- One agent or workflow you built and still maintain that removed real production work: inputs, outputs, QA, and the result.
- One quantified result (citations, qualified traffic, pipeline, or revenue) with the baseline, period, and how you attributed it.
- AI fluency you can show: how answer engines retrieve and cite sources, and which calls you still make yourself.
- Strong technical SEO, analytics, and prioritization judgment.
- Steady tenure; internal promotions are a big plus (same company counts fully).
- Ideal: specialist search agency or an in-house growth team that ships; B2B.
What this role is not
Not a content-volume role. Not vendor management or QC-only. Not a strategy-only seat that hands implementation to someone else. Not a people-manager seat. Not for occasional AI users.
How the process works
Application 7 short questions sent to you by email evidence review live working session (diagnose a site propose the AEO plan measurement plan) paid, time-boxed practical on two sample accounts with a live defense references. Redacted or recreated examples are welcome for confidential work.
Benefits
Medical, dental, and vision; life insurance; 401(k) with 4% match; unlimited PTO; monthly UberEats; MacBook; home-office stipend/setup. Subject to plan terms and eligibility.
Apply
Apply with your resume or LinkedIn. We will email you 7 short questions; answer them yourself. AI-written answers are disqualified.
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Single Grain, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Single Grain AI Hiring
Single Grain has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $150K - $150K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,580 tracked positions.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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