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About This Role
Santa Clara, CA
Job ID
68762507459
Date posted
07/08/2026
Department
Marketing
Eightfold is a global leader in an AI\-native enterprise talent platform, trusted by the world’s largest \& most respected Fortune 500 organizations. Our platform is built from the ground up, operating at scale across Azure and AWS, deployed in multiple regions globally, including IL4\-compliant environments for the US Government, supporting users in 100\+ countries and 30\+ languages. Today, Eightfold is at the forefront of agentic AI, delivering intelligent agents that actively drive outcomes across hiring and talent workflows, while much of the industry is still experimenting with prototypes. Backed by over $410M in funding and valued at $2B\+, we are defining the next era of agentic talent systems.
What sets Eightfold apart is not just the technology \& our mission, but the team behind it. We are a deeply technical, execution\-driven organization that values ownership, collaboration, and high standards. Our engineers, product leaders, and go\-to\-market teams work closely together — in person and across functions — to build systems that scale in the real world. If you’re excited to work on hard problems, move with urgency, raise the bar every day, and help build agentic systems that transform how the world works, Eightfold is the place to do it.
About the Role
We’re looking for a Marketing Campaign Manager who can use AI tools end to end to plan, build, and optimize campaigns for our agentic AI solutions for enterprises. You’ll own integrated campaigns across email, paid, web, and events, using platforms like Perplexity, Claude (Anthropic), and ChatGPT to research audiences, generate assets, orchestrate automations, and continuously improve performance.
Responsibilities
You will “own the full campaign lifecycle, with AI in the loop at every stage”:
- Plan and execute integrated campaigns (email, paid media, webinars, events, website) to drive pipeline and product adoption for our agentic AI offerings.
- Use AI tools (Perplexity, Claude, ChatGPT, Gemini) for market research, audience insights, and campaign ideation, including persona pain points, messaging angles, and competitive positioning.
- Draft and refine campaign assets with AI assistance: email sequences, landing pages, ad copy, nurture tracks, and event promotions, working closely with content and design.
- Build and manage campaign workflows in our marketing automation and CRM stack (Marketo plus Salesforce), including segmentation, lead routing rules, and scoring logic.
- Set up, QA, and launch campaigns end to end: list pulls, UTM tracking, form/landing page setup, testing, and post\-launch monitoring.
- Use AI agents and analytics to monitor performance, generate insights, and recommend optimizations on subject lines, offers, creative, and audience segments.
- Maintain a prompt and agent playbook documenting effective prompts, workflows, and automations that other marketers can reuse.
- Partner with demand gen, product marketing, sales, and marketing operations to align campaigns with product launches, ABM programs, and sales motions.
- Stay current on AI marketing trends and emerging tools, test new capabilities, and propose new AI\-powered use cases for our marketing programs.
What "AI\-powered" means in this role
Given you’re in an AI\-first org, it helps to be concrete:
- Research and insights: Use AI tools to run deep research on markets, accounts, and personas, then synthesize findings into campaign briefs.
- Content production: Use ChatGPT/Claude to generate first drafts of emails, ad variants, webinar outlines, and nurture trees, then edit for quality and brand alignment.
- Agentic workflows: Collaborate with marketing ops to define and run AI agents that handle tasks like audience segmentation, performance summaries, and experiment proposals.
- Experimentation: Use AI to propose and prioritize A/B tests, analyze results, and recommend next steps.
This is not a ‘prompt button pusher’ role – you’ll design and run AI\-assisted workflows that improve speed, quality, and outcomes across the entire funnel.
Qualifications
- 1–3 years of experience in B2B marketing, demand generation, or campaign management (internships and co\-op experience welcome).
- Hands\-on experience running campaigns in at least one marketing automation platform (e.g., HubSpot, Marketo, Pardot) and working with a CRM such as Salesforce.
- Demonstrated experience using AI tools like ChatGPT, Claude, Perplexity, Gemini or similar to support research, content creation, and/or campaign operations.
- Understanding of multi\-touch funnels, lead lifecycle stages, and key SaaS metrics (MQL, SQL, opportunity, pipeline, revenue).
- Comfortable working with data: building lists, reading dashboards, interpreting basic performance reports, and making recommendations.
- Strong writing and editing skills, especially for email and web copy.
- Detail\-oriented and process\-driven, with the ability to manage multiple campaigns and deadlines simultaneously.
- Curiosity and a builder mindset – you like experimenting with new tools, automations, and workflows.
- Clear written and verbal communication skills and a collaborative, service\-oriented mindset when partnering with marketing, sales, and operations teams.
Nice to Haves
- Experience marketing AI, SaaS, or enterprise software products.
- Exposure to agentic AI, AI agents for marketing, or similar autonomy\-oriented AI concepts.
- Familiarity with A/B testing, attribution reporting, and common B2B sales motions (ABM, product\-led growth, partner\-led).
We are a team of self\-starters who excel in their fields. We believe in giving you responsibility, not a task. We want you to have ownership and pride in your work and see your work's positive impact on your colleagues, our customers, and the world. We believe in providing transparency and support so you can do the best work of your career.
Hybrid Work @ Eightfold: At Eightfold, we believe our best work happens when we collaborate closely, learn from one another, and build together. We follow a hybrid work model that combines flexibility with a strong emphasis on in\-person collaboration to foster innovation, culture, and rapid execution.
Employees based near our Santa Clara, London, Bangalore, and Noida offices are expected to work from the office three days per week, as we believe regular in\-person engagement is essential to how we build high\-impact products and strong teams.
Eightfold.ai provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran or disability status.
Experience our comprehensive benefits with family medical, vision and dental coverage, a competitive base salary, and eligibility for equity awards and discretionary bonuses or commissions.
Please note that this role is only available in our HQ office in Santa Clara, CA. This role is hybrid.
\*Please note this role is open to SF Bay Area only and categorized as hybrid. The base salary range below is provided for pay transparency. Base pay is only one piece of our total compensation package as this role may be eligible for bonuses and equity awards. Compensation varies depending on a number of factors including qualifications, skills, competencies, and experience. Zone is determined by location.
Zone A (SF Bay Area, CA): Base Salary Range:
Our customer stories\- https://eightfold.ai/customers/customer\-stories/
Press\- https://eightfold.ai/about/press
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Eightfold, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Eightfold AI Hiring
Eightfold has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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