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About This Role
About the Role:
Most marketing teams still measure output in campaigns shipped. We measure it in decisions our agents make autonomously, and we want to widen that gap. As VP, Marketing Operations \& Agentic AI Systems, you will own the management of our martech stack and the strategy, architecture, and execution of the agentic layer that runs Sidetrade marketing: lead scoring, enrichment, prospection, campaign orchestration, and attribution. You will define what "production\-ready" means for AI agents at Sidetrade, build and lead the team that ships them, and hold accountability for measurable business outcomes reported at board level.
This is a builder\-leader role. You will set the vision and roll up your sleeves. You will not manage a function that runs on slide decks.
About Sidetrade:
Sidetrade is an AI company, listed on Euronext Growth, on a mission to revolutionize the way enterprises unlock value from their customers leveraging its Order\-to\-Cash Intelligence platform and its Data Lake.
We’re proud of our 38 nationalities and these diverse perspectives drive our innovation, one team culture and a customer\-first mindset. Siudetrade is positioned as a Gartner® Magic Quadrant™ Leader since 2022\.
We value passion over perfection. So, if you’re eager to learn and bring great energy, we want to hear from you. Be you. Grow with us.
Curious about Sidetrade? Catch the Sidetrade Inside Out podcast.
Requirements What You will do:
- Define and own the multi\-year roadmap for AI\-powered marketing operations, aligned to revenue targets and approved at exec level.
- Set the standard for what constitutes a production\-grade agent at Sidetrade: Eevaluation suites, guardrails, observability, and retirement criteria.
- Advise the CMO and CEO on emerging AI capabilities and their commercial implications for pipeline and revenue.
- Lead delivery of Deliver production agents for lead scoring, enrichment, MQL handoff, prospection, and revenue attribution, co\-owned with Sales, BDRs, and Demand Gen.
- Replace at least five manual marketing workflows with production agents in the first 18 months, with documented time savings and quality parity or better.
- Own prompt libraries, tool definitions, and evaluation suites that govern agent quality across the team.
- Stand up and own the observability layer: cost per task, accuracy, drift, human escalation rate.
- Report the performance of the agentic layer monthly to the exec team and quarterly to the board.
- Quantify pipeline and revenue influenced by AI\-driven workflows; defend the numbers.
- Own and manage the Martech stack: Marketo, Salesforce, Salesloft, 6Sense, ZoomInfo, Cognism, LinkedIn Sales Navigator, LetSignIt, Asana, Kinsta, GoToWebinar, Wistia, Google GTM, Search Console, Analytics, Ads, Zapier, Proofjump.
- Cut the stack by at least 25% on cost or tool count in the first 18 months against measurable value.
- Evaluate, onboard, and retire vendors. You hold final sign\-off on Martech investment.
- Provide enablement to marketing and sales users in collaboration with the department leaders
- Hire, develop, and retain a team of marketing engineers and operations specialists.
- Define roles, set standards, and create a culture of measurement and continuous improvement.
- Partner with Product and Engineering leadership to align shared infrastructure and tooling.
What You Will Bring:
- 12\+ years in B2B SaaS marketing, with at least 4 years in a senior leadership role owning technology and operations.
- Proven track record shipping production AI\-driven workflows or agents that real users and revenue processes depended on. Sandbox demos do not count.
- Deep fluency with Marketo, HubSpot, or Pardot, plus Salesforce. You have owned these platforms at scale.
- Hands\-on with n8n, Make, or Zapier: multi\-step flows, error handling, API integrations without writing production code from scratch.
- Expert\-level command of LLM tools (Claude, ChatGPT, or equivalent): prompt design, AI\-in\-workflow architecture, and clear judgment on reliability limits.
- Able to scope work for engineering teams, spec clearly, and validate output. You read JSON and API docs fluently.
- Track record of board\-level reporting on marketing technology performance and ROI.
- Strong communicator: you translate agentic complexity into commercial outcomes for exec and investor audiences.
Benefits
- Competitive executive compensation package including base salary and performance incentives.
- Hybrid work model: a flexible mix of in\-office and remote days.
- Health \& wellness: medical coverage, life insurance, and wellness programs.
- Generous paid leave plus public holidays.
- Learning, mentorship, and career advancement support at exec level.
- Active Social Club with regular team events across our international offices.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Sidetrade, 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 $181,170 based on 12,692 positions with disclosed compensation.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sidetrade AI Hiring
Sidetrade has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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