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About This Role
Grow Your Career Where Talent Meets Innovation
Summary
The purpose of this role is to manage and optimize AI-supported lead engagement, website content, and digital campaign execution to support demand generation and move prospects through the customer journey. This position serves as the internal subject matter expert for Qualified (Piper X), overseeing campaign messaging, AI SDR workflows, website and blog content updates, and SEO/AEO-driven content improvements. The role also supports marketing performance through management of key digital channels and tools, including Google Ads and ABM platforms, while helping ensure alignment across regions and teams.
Essential Functions/Responsibilities
Under the guidance of senior team leaders, AI and Website Content Marketing Manager will:
- Develop overall Qualified strategy for Marketing/SDR, including campaign and nurture strategy, guided by customer journey
- Serve as the primary internal subject matter expert for Qualified and Sophia (Piper) X
- Develop and approve messaging (working with Product Marketing Managers/MarCom) for all Marketing and SDR Qualified campaigns
- Manage and optimize AI SDR workflows, offers, and engagement logic.
- Manage and approve Sophia programming updates
- Manage AEO and SEO recommendations from all platforms, and associated improvements (primarily from a content perspective, with some technical knowledge preferred)
- Oversee and execute new website content and page updates across regions
- Oversee blog schedule based on podcast and webinar schedule, post and update blogs
- Manage Google Ads with agency support
- Manage Rollworks or other ABM tool (with strategy support from Senior MarTech Manager)
- Select tools for AEO/SEO management and recommendations with oversight from Senior MarTech Manager
Minimum Requirements
- Bachelor's degree in marketing, communications, business, or a related field (or equivalent work experience).
- Experience in B2B marketing with prior exposure to life sciences, healthcare, or similar industries.
- 2–5 years of marketing experience, including at least 2 years working with marketing automation platforms (HubSpot required), Google Ads and/or website content.
- At least 2 years of graphic design experience.
- Exposure to AI-driven campaigns and marketing with willingness to continuously learn and drive adoption forward.
- Knowledge of SEO and AEO concepts including keyword research, on-page/technical SEO fundamentals, and website performance monitoring and recommendations.
- Familiarity with Salesforce or other CRM systems.
- Understanding of integrated/data transfer applications between marketing tools is a plus.
Preferred Requirements
- Basic proficiency with HubSpot and understanding of CRM workflows.
- Basic understanding of email marketing best practices and compliance requirements (GDPR, CAN SPAM). - Experience with survey tools (Google Sheets, SurveyMonkey, Qualtrics).
- Eagerness to learn and apply SEO best practices and AI-enabled campaign techniques.
- Familiarity with the Qualified platform or other conversational marketing tools (preferred)
- Attention to detail and accuracy in data handling.
- Ability to manage multiple tasks/projects with supervision.
- Enthusiasm for modern, data-driven marketing with AI applications
Salary range is $90,000-$110,000
This salary (or salary range) is merely an estimate and may vary based on an applicant’s skills and prior relevant experience.
Crown Bioscience is committed to a diverse and inclusive workplace. Crown Bioscience is an equal opportunity employer and does not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Crown Bioscience, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Crown Bioscience AI Hiring
Crown Bioscience has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $110K - $110K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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