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About This Role
Who We Are
Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600\+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world's most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20\+ years strong, Kargo has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland.
Who We Hire
Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn't stop to ask what's next, because they're already building it.
Salary Range: $140,000 \- $180,000
Work Type: Hybrid
Mission
As the AI Engineer at Kargo, you will architect, build, and scale AI\-powered products and automations for Kargo's commercial organization. Operating within the Data \& AI team, you are the connective tissue between revenue teams and AI infrastructure — proactively identifying high\-value use cases, building intelligent workflows and agentic applications, and deploying trustworthy systems across Salesforce, Snowflake, Slack, and other internal platforms. You're both hands\-on and capable of owning the strategic roadmap for AI operations at Kargo.
Outcomes \- What Success Looks Like in 6\-12 Months
- At least 3 high\-impact AI automations are live and actively used by commercial teams — measurably reducing manual work or improving data quality across Salesforce, Slack, or Snowflake
- A governance model is in place covering prompt engineering standards, audit trails, and a feedback loop that drives continuous iteration
- Cross\-functional stakeholders trust and use the tools you've built, and Kargo's Data \& AI leadership has a clear, prioritized AI Ops roadmap that you own and drive
- You've established yourself as Kargo's internal thought leader on applied AI — the person teams come to when they have a problem AI might solve
Skills \- Core Capabilities
Design \& Automation
- Design, build, deploy, and maintain AI\-powered automations and agent workflows using modern orchestration frameworks — LangGraph, n8n, OpenAI Responses/Agents tooling, MCP\-compatible architectures — with integrations across Salesforce, Slack, Snowflake, Atlassian, Google Workspace, Looker, and Airtable
- Translate business pain points into modular, extensible automation flows that are observable, debuggable, and fault\-tolerant; proficient in Python or JavaScript for custom connectors and scripting
- 5–8\+ years in systems automation, internal tools, or process/data engineering; hands\-on with orchestration platforms such as n8n, LangGraph, Zapier, or Make; strong familiarity with SaaS APIs and system interoperability
AI Agent Deployment
- Build production\-grade LLM applications — agent workflows, retrieval systems, internal copilots — using ChatGPT Enterprise and related LLM APIs for knowledge surfacing, workflow routing, decision support, and dynamic content generation
- Maintain a governance model for prompt engineering, agent testing, and audit trails; leverage AI\-assisted development tools (Claude Code, Cursor, Codex) to accelerate velocity; familiar with evaluation and observability frameworks for LLM applications
Internal Enablement \& Strategy
- Work cross\-functionally with Sales, Client Services, Media Strategy, Marketing, Product, and Ops to discover automation opportunities, prototype quickly, document tooling, and drive self\-service adoption
- Own and communicate the AI Ops roadmap to Data \& AI leadership — prioritized by business impact, sequenced by feasibility, and grounded in real discovery with commercial teams
Nice to Have
- Prompt libraries, embeddings\-based retrieval, or vector databases (Pinecone, Weaviate) and RAG pipelines
- Retool or Streamlit for lightweight internal UIs; ArgoCD or Kubernetes CI/CD experience
Competencies \- Behaviors We Like to See
Builder's Instinct
- Ships fast, iterates on real feedback, and knows when to build vs. buy — comfortable defining the problem and executing without a fully specified brief
Cross\-Functional Fluency
- Translates between engineers and revenue leadership, earns trust by delivering things that work, and stays close to adoption after deployment
Bias for Impact
- Prioritizes automations that move a real business metric and measures success by adoption and friction reduction — not lines of code
Ownership \& Accountability
- Owns the roadmap end\-to\-end, communicates proactively, flags blockers early, and treats internal users like customers
Growth Mindset
- Stays current on the LLM and AI agent landscape, applies new tooling when it matters, and shares knowledge generously to raise AI fluency across teams
Our Laurels
- AdAge Best Places to Work
- ThinkLA Partner of the Year
- Built In Best Places to Work
- Cynopsis 2025 Top Women in Media \- Jeannine Shao Collins
- Martech Breakthrough Awards \- Best Overall Adtech Company
- Digiday Media Awards Best Event
- Cynopsis Media Impact Awards\-Best CTV Platform
- Martech Breakthrough Awards\-CTV Innovation
- Adweek Media Plan of the Year Awards \- Best Use of Insights
Follow Our Lead
- Big Picture: kargo.com
- The Latest: Instagram (@kargomobile) and LinkedIn (Kargo)
Salary Context
This $140K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Kargo, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Kargo AI Hiring
Kargo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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