Interested in this AI/ML Engineer role at Treasure AI?
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Treasure AI:
Treasure AI is the agentic experience platform built to acquire, retain, and grow your most valuable customers. Powered by AI, Treasure AI is shaped by human creativity and always\-on through continuous, context\-driven action.
Furthermore, Treasure AI employees are enthusiastic, data\-driven, and customer\-obsessed. We are a team of drivers—self\-starters who take initiative, anticipate needs, and proactively jump in to solve problems. Our actions reflect our values of honesty, reliability, openness, and humility.
Your Role:
As an AI Deployment Consultant at Treasure Data, you sit at the intersection of customer success and AI\-powered innovation. This is a hands\-on, customer\-facing Professional Services role \- you work directly with Fortune 1000 clients to deliver value from Treasure AI's data and experience platform, from initial implementation through ongoing use case expansion.
The role operates across two interconnected lanes. The first is CDP foundation work: while you don't own full CDP implementations, you bring genuine fluency in data flow, orchestration, and the implementation lifecycle. On Foundations engagements, you come in toward the end to deliver customer training and enablement, and to build a strategic use case roadmap, ideally AI\-focused, that sets the customer up for long\-term success.
The second, and increasingly primary, lane is AI deployment. This is where the practice is investing and where your impact will grow. You work with both new and existing customers to deploy AI agents and skills via Treasure AI Studio, designing and building autonomous processes and agent\-based solutions that address real business needs. You don't just configure tools, you help customers understand what's possible, shape the solution, and own the outcome.
You're comfortable managing multiple customer engagements simultaneously, including escalations, and you bring the judgment to prioritize and adapt without losing quality. You're also a builder at heart: eager to shape the AIDC practice itself, contributing to playbooks, patterns, and methodologies, not just execute within it.
Responsibilities:
- Lead requirements elicitation and workshop sessions with business and technical stakeholders to uncover customer objectives, data challenges, and AI\-driven solution needs.
- Lead requirements elicitation and workshop sessions with business and technical stakeholders to uncover customer objectives, data challenges, and AI\-driven solution needs.
- Analyze and document business processes, user journeys, data flows, and technical requirements, translating business goals into actionable solution designs.
- Partner with Solution Architects and Data Engineers to design and configure Treasure Data\-based use cases, including segmentation, audience activation, personalization, AI\-powered recommendations, and reporting.
- Design and deploy AI agent and skill\-based solutions via Treasure AI Studio, building autonomous workflows that address specific customer business problems.
- Configure no\-code/low\-code elements of CDP workflows, user interfaces, AI agent pipelines, and data activation tools to meet client needs.
- Execute hands\-on testing, validation, and troubleshooting of solution workflows, including AI and agentic components, prior to launch.
- Deliver customer enablement: workshops, training, and guided handoffs for both business end users and technical client teams across CDP and AI capabilities.
- On Foundations engagements, own the late\-stage training program and strategic use case roadmap, ensuring customers leave with a clear path to AI\-powered activation.
- Manage multiple active customer engagements concurrently, including escalations, with the organizational discipline to deliver quality across all of them.
- Advise clients on best practices related to responsible AI use, data governance, and compliance within the Treasure Data ecosystem.
- Actively contribute to building the AIDC practice \- developing playbooks, reusable frameworks, and delivery methodologies that raise the bar for the whole team.
- Own documentation: requirements specs, functional design documents, data mapping sheets, AI workflow diagrams, and end\-user guides.
Job Requirements:
- 5\+ years of experience in functional consulting, business analysis, or solution delivery, preferably in SaaS, martech/adtech, or data\-driven environments.
- Proven expertise gathering and interpreting complex requirements from diverse business and technical stakeholders.
- Hands\-on experience designing and deploying AI\-based solutions — agentic workflows, LLM applications, or intelligent decisioning — in a client\-facing context.
- Genuine fluency in CDP or data platform concepts: data flow, segmentation logic, audience activation, and data transformation.
- Ability to break down ambiguous business problems and deliver structured, documentation\-driven solutions that fit within broader project architecture.
- Demonstrated ability to manage multiple customer engagements simultaneously, maintaining quality and stakeholder trust across all of them.
- Strong communication and facilitation skills — comfortable leading workshops, delivering training, and translating technical concepts for non\-technical audiences.
- Genuine curiosity and passion for AI — someone who follows the space, experiments independently, and brings energy to the AI deployment work, not just openness to it.
- Collaborative and adaptable, with the initiative to help build and shape a growing practice rather than simply follow an established playbook.
- Bachelor's degree or equivalent practical experience.
Preferred Qualifications
- Experience with Treasure AI platform, or equivalent enterprise CDP platforms, including configuration, onboarding, and functional design.
- Background in customer data management, digital marketing execution, or data privacy/compliance (GDPR, CCPA).
- Certifications or demonstrated skills in building, testing, or enabling AI/LLM/chatbot/decision agent solutions.
- Experience working on multi\-functional project teams with Solution Architects, Data Engineers, and Engagement Managers.
- Strong consulting background, with experience advising enterprise clients on technology strategy and change management.
Physical Requirements:
Working out of the New York office according to our “Global Hybrid Working Policy.”
Travel Requirements:
Up to 20% travel.
Perks and Benefits (US):
Our benefit package showcases our culture of care and empathy with
- Comprehensive medical, dental, vision plans and Employee Assistance Program (EAP)
- Competitive compensation packages
- Company paid life insurance 3x salary
- Company paid short\- and long\-term disability coverage
- Retirement planning (401K) with 4% company match
- Restricted Stock Units (RSU)
- Flexible Time Off (FTO)
- Up to 26 weeks paid parental leave including a post\-partum night nurse
- Comprehensive support and access to care for everyone, everywhere through Carrot \- our global reproductive health and family\-building benefit.
Our Dedication to You:
We value and promote diversity, equity, inclusion, and belonging in all aspects of our business and at all levels. Success comes from acknowledging, welcoming, and incorporating diverse perspectives.
Diverse representation alone is not the desired outcome. We also strive to create an inclusive culture that encourages growth, ownership of your role, and achieving innovation in new and unique ways. Your voice will be heard, and we will help amplify it.
Agencies and Recruiters:
We cannot consider your candidate(s) without a contract in place. Any resumes received without having an active agreement will be considered gratis referrals to us. Thank you for your understanding and cooperation!
This description captures the core of the role today. As we adopt AI and new ways of working, responsibilities may evolve, and we encourage team members to take initiative, lean into change, and help expand the impact of their role beyond what’s listed here.
Salary Context
This $130K-$145K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Treasure AI, 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 in Demand for This Role
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. This role's midpoint ($137K) sits 37% below the category median. Disclosed range: $130K to $145K.
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.
Treasure AI AI Hiring
Treasure AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $145K - $145K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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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