Interested in this AI/ML Engineer role at KONG?
Apply Now →About This Role
Location
Colorado, United States
Employment Type
Full time
Location Type
Remote
Department
All Cost CenterMarketingProduct Marketing
Compensation
- $153,090 – $251,505 • Offers Equity • Offers Bonus
Kong has different base pay ranges for different work locations within the United States and Canada, which allows us to pay employees competitively and consistently in different geographic markets. Compensation varies depending on a wide array of factors, including but not limited to specific candidate location, role, skill set and level of experience. Certain roles are eligible for additional rewards including sales incentives depending on the terms of the applicable plan and role. Benefits may vary depending on location. US based employees are typically offered access to healthcare benefits, a 401(k) plan, short and long term disability benefits, basic life and AD&D insurance, among others.
Are you ready to unlock intelligence?
If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box - we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others.
About the Role:
Every enterprise deploying AI at scale faces the same challenge: governing an explosion of LLM, MCP, and agent traffic while giving developers a way to discover and consume AI resources safely. This role owns the technical narrative at the heart of that story. As Senior Technical PMM, you'll drive positioning, evangelism, and competitive strategy for Kong's AI traffic management capabilities — the AI gateway and guardrails that secure and control AI traffic in production — as well as our registry and discovery experience spanning AI, MCP, and agent registries. This is a core technical marketing role on the AI team, covering the most contested competitive ground in the market.
What You’ll Do:
- Own positioning and messaging for AI traffic management — define and evolve the technical narrative for Kong's AI gateway and guardrails capabilities, including LLM routing, policy enforcement, content safety, and multi-agent traffic governance
- Drive the registry and discovery story — build positioning for AI, MCP, and agent registries, covering listing, registration, governance, and the developer discovery experience
- Lead technical field enablement for your domain — own product enablement within your domain for Kong Solution Engineers, Customer Success Managers, and other technical field roles
- Serve as technical evangelist — represent Kong at industry events, analyst briefings, webinars, and customer conversations; produce technical content including demos, blogs, whitepapers, and reference architectures
- Produce deep technical content — build demos, reference architectures, technical whitepapers, and documentation-adjacent content that helps architects and security teams evaluate and adopt Kong's approach
- Partner with product on roadmap narrative — work closely with product management to shape how new capabilities are framed, named, and sequenced for maximum market impact
- Fuel launches and campaigns — deliver technical positioning, proof points, and demo assets as structured inputs to the AI PMM's launch and campaign motions
- Mentor and elevate — as the senior-most technical PMM on the team, raise the bar for technical storytelling across the AI portfolio
What You'll Bring:
- 7+ years of experience in product marketing, product management, or solutions engineering—ideally at an AI/ML platform company
- Genuine hands-on interest in AI infrastructure: you follow developments in LLM deployment, agentic frameworks, Model Context Protocol, and AI governance; you can speak credibly to AI platform engineers without bluffing
- Proven ability to translate complex technical concepts into crisp narratives that land with both technical practitioners and C-suite buyers
- Highly collaborative—you thrive working across Product, Marketing, Sales, and Partner teams
- Comfortable owning a stage: keynote presentations, analyst briefings, customer roundtables, and demo-heavy sessions are energizing, not intimidating
- Experience in open-source software, SaaS platforms, and enterprise go-to-market motions strongly preferred
- Familiarity with the AI connectivity landscape—including awareness of competing and complementary vendors across API gateways, AI observability, vector databases, IPaaS, and agentic orchestration frameworks—is a strong plus
#LI-KH1
About Kong:
Kong Inc., the AI Connectivity Company, is building the connectivity layer of AI. Trusted by the Fortune 500® and AI-native startups alike, Kong’s unified API and AI platform enables organizations to secure, manage, accelerate, govern, and monetize the flow of intelligence across APIs and AI traffic — on any model, any cloud. For more information, visit www.konghq.com.
Compensation Range: $153,090 - $251,505
Salary Context
This $153K-$251K range is above the median 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 KONG, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($202K) sits 5% below the category median. Disclosed range: $153K to $251K.
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.
KONG AI Hiring
KONG has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span CO, US, US. Compensation range: $135K - $251K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.