Interested in this AI/ML Engineer role at KUNGFU.AI?
Apply Now →About This Role
KUNGFU.AI is a management consulting and engineering firm focused exclusively on artificial intelligence. We empower CEOs and senior executives to leverage the full potential of AI so they remain competitive in a rapidly evolving world.
Our expert team delivers AI strategy and bespoke production\-grade solutions that allow clients to rapidly realize value. We stand apart because we implement our AI strategies into production quickly, safely, and responsibly.
Outside of our virtual walls we are painters, foodies, cocktail connoisseurs, musicians, crossword\-doers, athletes, dungeon masters, non\-profit board members, and more. We'd welcome the opportunity to meet you.
As a Senior AI Project Manager at KUNGFU.AI you will work with a delivery team of Machine Learning Engineers, Data Scientists, and AI Strategists to successfully deliver projects to our clients.
Here's what you'll be doing:
Project and Delivery Management:
Overseeing projects from inception to completion, ensuring high\-quality, on\-time, and on\-budget delivery while managing scope, risks, and resources.
- Leading end\-to\-end project management
- Driving project governance (scope, staffing, SOWs, timelines, risk mitigation)
- Partnering with leads to keep clients aligned and navigate escalations
- Ensuring deliverables exceed expectations
- Shaping realistic scopes that enable sustainable delivery
- Overseeing financial health (budgets, forecasts, invoicing)
Team Leadership \& Cross\-functional Collaboration:
Emphasizing people management, mentoring, and facilitating effective collaboration across engineering and strategy project teams and client stakeholders.
- Coaching and mentoring teammates, clients, and new hires
- Championing delivery excellence through retrospectives, continuous improvement, and process enhancements
- Driving and facilitating for clarity and alignment
- Partnering with project leads to ensure high\-quality, timely deliverables
Strategic Product Ownership (as applicable):
Responsibilities taken on when acting in a Product Owner capacity within engagements, with an emphasis on translating business needs into actionable product development.
- Translating client and end\-user needs into solution requirements
- Acting as the voice of the customer to guide solution development
- Managing and refining the product backlog
- Making real\-time scope tradeoff decisions to maintain value and momentum
- Bringing a strategic lens to delivery, identifying opportunities for growth and innovation
What we're looking for:
- Substantial experience delivering high\-complexity software projects in a consulting or professional services environment, ideally involving AI, data, or emerging technologies, with deep SDLC knowledge
\- Exceptional EQ \- someone who can intuit team dynamics and client needs, anticipate challenges several steps ahead, and clearly translate those insights across internal and external stakeholders
- Proven track record of successfully managing senior client relationships while navigating challenging conversations and ambiguous objectives
- Someone who has experience managing multiple concurrent projects or programs (typically 2\-3 depending on complexity/size) from ideation and initiation through final delivery and client acceptance
- Comfort navigating ambiguity, shifting priorities, and experimental or proof\-of\-concept work
- Clear and adaptable written and verbal communication skills for both technical and non\-technical audiences
- Someone with experience with agile and traditional delivery methodologies and who enjoys spanning across them.
- Professional and academic credentials (PMP, CSM, or equivalent) are a nice bonus, but not required
- A systems thinker who enjoys refining and evolving delivery processes to improve quality, scalability, and collaboration
- A collaborative and empathetic leader who invests in the success of their team and fosters a culture of shared ownership and continuous learning
What's in it for you:
This is a full\-time position that is remote. Compensation includes competitive salary, company stock grant, top ('gold') level of health, dental and vision insurance (KUNGFU.AI covers 100% of your premiums and 50% for any dependents), health and dependent flexible spending accounts, short\- and long\-term disability insurance, generous vacation (that you're encouraged to take), paid parental leave, and more. Please send your resume along with a cover letter explaining why you're passionate about the idea of joining KUNGFU.AI and a great fit for this role.
KUNGFU.AI Culture
We are committed to building an inclusive culture that's professional and effective, while also fun, collaborative and open. We are proud to be the first company to take The Startup Diversity and Inclusion Pledge, which one of our co\-founders started. We actively encourage people from underrepresented groups to apply. We believe that a diverse and inclusive workforce fosters more creative ideas, conversations, and results.
We also believe in using AI for Good in both our client projects and our corporate philanthropy.
Our values are incredibly important to how we show up for each other, our clients, and for society at large. We expect all of our team members to be:
Inquisitive, in that they have an innate desire to learn and teach.
Inventive, in that nothing (ego, fear, etc.) gets in the way of innovation.
Open, in that they willingly share their learnings and experience with all.
Caring, in that they prioritize self\-care, empathy, and an understanding of others.
Trustworthy, as that underpins all of the above.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At KUNGFU.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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
KUNGFU.AI AI Hiring
KUNGFU.AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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