Book cover titled 'Code with Kate' by Kate H E Emery with a light, colorful cloudy background.

Think strategically. Code confidently. Build intentionally.

I'm Kate—software engineer, AI/ML grad student, FRC alumna, and 15-year FIRST volunteer. I've mentored championship programming teams, inspected thousands of robots, worked fieldside and in the pits, and built tech careers at IBM, Boeing, and startups. I believe your career should be built from strategic stepping stones, not desperate job searches. Whether you're figuring out how to run a programming team, navigating the brutal tech job market, or building ML systems that actually work, I'm here to share what I've learned from every angle.

Who This is For

FIRST Robotics Mentors & Volunteers

You're building the future of engineering, but there's no playbook for running a championship programming team, navigating volunteer roles, or translating competition chaos into actual learning.

I bring perspectives from three angles:

As a mentor: How championship teams structure programming education and run their software teams
As a volunteer: What works (and fails) fieldside, in the pits, and as a lead robot inspector
As an alumna: How to become the mentor or volunteer who actually makes a difference

Whether you're figuring out how to teach programming concepts that stick, wondering what inspectors actually look for, or trying to understand how championship teams operate—I've been in all those roles for over a decade.

Tech Professionals & Students

The 2025 job market is brutal. You've been told to "just apply" and "network more," but nobody's teaching you how to build a career that doesn't depend on luck.

I help you:

Turn any role into a strategic stepping stone
Advocate for yourself without feeling pushy
Build relationships that open doors (not transactional networking)
Navigate imposter syndrome and perfectionism

ML/AI Engineers & Hiring Teams

You need engineers who understand not just how to use TensorFlow, but how the algorithms actually work—and can deploy systems that don't fall apart in production.

I bring:

Deep CS foundations + AI/ML specialization (WGU MS)
Production experience from Boeing to startups
Systems thinking from teaching robotics to building scalable applications
Communication skills proven through mentoring and speaking

My Philosophy: Career as Journey, Not Destination

There is no perfect "dream job" waiting at some mythical company. There's only your dream career— a series of strategic roles that leverage your strengths, align with your values, and move you toward the professional life you're building.

I learned this at Boeing, where a promotion I didn't get taught me more about career advancement than years of "doing great work" ever could. I learned it again choosing Computer Science over Data Science for my AI/ML master's—not because one is better, but because one fit how I think and what I want to build.

Every role is a stepping stone. Every "no" is data. Every rough moment fertilizes the amazing ones. The question isn't "Am I in my dream job?" It's "Am I building toward the career I actually want?"

What You’ll Find Here

Real Talk About Tech Careers

No generic "10 tips to land a FAANG job" listicles. Instead, honest reflections on promotions I didn't get, career pivots that worked (and didn't), and the messy reality of building a tech career when you're a woman who refuses to play office politics.

Strategic FIRST Robotics Guidance

Practical wisdom from 15+ years volunteering and 10+ years mentoring at championship level. Not theory from someone who watched regionals once—insights from the perspective of a mentor who's run programming teams, a volunteer who's worked fieldside and in the pits, a lead robot inspector who knows what passes (and why), and an alumna who understands this community from every angle.

Technical Deep Dives That Actually Help

Programming concepts explained like coffee orders. AI/ML from a builder's perspective, not just a user's. Systems thinking that translates from teaching students how code controls physical robots to building production ML pipelines.