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About This Role
Role Overview
You've spent years learning to code, build systems, solve technical problems. Now use that analytical mindset in a new way.
As our Technical Marketing Specialist, you will:
- Deeply understand our AI and Agentic solutions (technical depth matters here)
- Explain complex tech concepts to non-technical buyers (articulate what engineers built)
- Create technical content: case studies, technical blogs, architecture diagrams, demos
- Work directly with our engineers to understand product capabilities
- Help sales teams explain technical value to enterprise customers
- Measure what works and iterate based on data
What You'll Own
Technical Translation & Content
- Translate engineering specifications into buyer-friendly language
- Write technical blog posts explaining AI/Agentic concepts
- Create case studies with technical depth AND business outcomes
- Build technical diagrams, architecture explanations, how-it-works content
- Answer complex technical questions from prospects
Participate in technical demos and sales calls *
Campaign Support & Execution
- Help execute technical campaigns targeting engineers at buyer companies
- Support webinars with technical content (code examples, architecture, deep dives)
- Create technical comparison content (our solution vs. competitors)
- Support technical Q&A content and FAQs
- Track engagement on technical content (data-driven)
Research & Analysis
- Research how enterprises use AI/Agentic solutions
- Analyze competitor technical offerings and positioning
- Study industry trends in AI/automation/ML
- Collect customer feedback on technical capabilities
- Document product capabilities for sales/marketing use
Sales Enablement
- Create materials that help sales engineers close deals
- Develop technical FAQs and response templates
- Build technical battlecards (how to position against competitors)
- Support pre-sales technical discussions
Create technical value proposition documents *
Why This Is Perfect For Engineers
✅ Use Your Technical Skills: Code understanding, system thinking, analytical rigor—all valued here
✅ Data-Driven: Everything measured. What content works? What messaging converts? Prove it with data.
✅ Problem-Solving: How do you explain a complex distributed system to a CEO? How do you position automation vs. human labor? Solve these communication problems.
✅ No BS Nonsense: We don't do vanity metrics ("10K impressions!"). We care about business outcomes. Engineers respect that.
✅ Learn Business: See how your technical work drives leads, pipeline, revenue. Understand the business side of technology.
✅ Credibility: Talk to engineers and technical decision-makers as a peer. You speak their language.
✅ Career Path: Technical marketing is a legitimate career (not a fallback). Step into this, become an expert, position for product management or director roles.
Requirements
- Computer Science degree OR engineering degree (any discipline)
- Strong analytical and logical thinking
- Comfortable with technical complexity (don't need to build it, but must understand it)
- Attention to detail and precision (marketing copy for technical products must be exact)
- Can explain complex systems in writing (clear, structured, logical)
- Curious about how things work
- Data-driven mindset (measure what works)
Communication Skills
- Can write clearly and precisely (not creative, but accurate)
- Can explain technical concepts to non-technical people
- Strong communication (written more than verbal—this is technical content)
- Can diagram systems and explain architecture
Attention to detail in writing *
Nice-to-Have Technical Skill
- Python, JavaScript, or any programming language
- Understanding of APIs and system integration
- Knowledge of AI/ML/automation concepts
- Database or SQL experience
- Git, GitHub, or version control
- Linux/command line basics
- Cloud platforms (AWS, GCP, Azure)
Understanding of enterprise software architecture *
Integrant is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity, genetic information, or any other protected characteristic.
Benefits
Salary**: $50,000–$65,000 annually
- Based on education, technical background, communication skills
- SB 642 compliant (California transparency)
Salary Context
This $50K-$65K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Integrant, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($57K) sits 73% below the category median. Disclosed range: $50K to $65K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Integrant AI Hiring
Integrant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $65K - $65K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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