Matt Kilmer
Product Manager · Software Engineer · Musician
About
Product manager with 10 years experience building consumer products in music and technology. Currently at Viewcy working on growth for an event ticketing and streaming platform.
I've shipped music apps to over a million users, optimized funnels from signup through monetization, and built products that balance rapid experimentation with longer-term strategic bets. Most recently I've been deep in AI/LLM applications - shipping tools that work in production, not just demos.
I also write code. Recent projects: a Shopify app optimizing product listings at scale, a Slack bot that analyzes codebases and creates pull requests, and an interactive audio-visual instrument playable in your browser.
Music Background
Before product management, I spent a decade as a professional musician and producer. Berklee-trained, toured with Lauryn Hill and Reggie Watts, coordinated music for an Emmy Award-winning TV show. That world taught me about creating for audiences, iterating under uncertainty, and the difference between what's technically impressive and what actually resonates. It's why I care about building products that feel as good as they perform.
Experience
ResumeSenior Product Manager
- •Oversee core product roadmap for event ticketing and live streaming platform
- •Partner with executive stakeholders to define roadmap and set goals in-line with company vision and mission
- •Balance the needs of multiple user classes (artists, fans, sponsors) to drive success for all customers and stakeholders
- •Own product specification and development process, interfacing with engineering, design, UX, marketing, and analytics teams to consistently deliver product releases on time
Founder and CEO
- •Oversee all aspects of business operations and strategy for a boutique eco-conscious health and beauty brand
- •Establish and maintain relationships with vendors and strategic partnerships
Senior Product Manager
- •Led product development lifecycle for flagship music app and interactive digital music service
- •Measured and evaluated product optimizations for business impact including conversion, retention and LTV
- •Built the team from scratch - hired across product, engineering, sales, and design
- •Worked with major labels on licensing and content partnerships
Product Manager
- •Set product vision and strategy inline with business objectives for music software startup
- •Defined releases, oversaw user testing, prioritized features, owned product roadmap
- •Built three apps - one of them took off unexpectedly
Music & Production
Music Coordinator
- •Oversaw music production for a comedy series - hiring musicians, managing sessions, delivering assets
- •Five seasons of figuring out how to make creative collaboration work under deadlines
Touring / Session Musician
- •Played sessions and tours across genres - learned a lot about showing up prepared and adapting on the fly
Projects
Production SaaS helping Shopify merchants optimize product listings at scale through intelligent content generation and bulk processing.
Clean Beauty Advisor
AI-powered beauty product ingredient analysis with personalized recommendations
Production SaaS platform analyzing 100k+ beauty products with GPT-4o, helping users make informed choices based on their skin profile and ingredient preferences.
Interactive music creation tool that transforms touch into particle animations and synthesized soundscapes - playable by anyone, no training required.
Claude Slackbot
Autonomous AI development assistant operating directly in Slack
Production bot that analyzes codebases, generates fixes, creates pull requests, and deploys previews - all from natural language Slack messages.
TradingGPT
Natural language to executable trading strategies with backtesting
Platform translating plain English into validated, backtested algorithmic trading strategies with risk management and paper trading deployment.
Notes on Building with LLMs
The demo-to-production gap
Demos optimize for wow moments. Production systems optimize for reliability, cost efficiency, and graceful degradation. The hardest problems usually aren't the AI - they're context management, quality evaluation at scale, and handling non-deterministic outputs in deterministic systems.
Trust beats capability
The biggest blocker to AI product adoption isn't model capability - it's user trust. Quality control mechanisms (preview before apply, rollback capabilities, validation layers) matter more than using the newest model. Users need confidence that AI won't break their production systems.
The boring stuff matters most
Prompt caching, context window management, knowing when to use a smaller model - these ended up mattering more than clever prompting. Cost-per-request directly impacts product viability. It's not an optimization problem, it's a design constraint.
Measure before you scale
How do you know if your AI product is improving? Traditional metrics don't apply to subjective outputs. Human evaluation pipelines, quality scoring systems, failure case analysis - you can't improve what you can't measure, even when outputs are probabilistic.
Anticipate misuse
AI products carry unique responsibilities. Input validation to prevent prompt injection, output filtering, rate limiting, audit logging. Building responsibly means anticipating misuse, not just optimizing for the happy path.
Skills
AI/ML
- •LLM Integration & API Design
- •Prompt Engineering & System Design
- •Context Window Optimization
- •Structured Outputs & Function Calling
- •Streaming & Real-time Processing
- •Prompt Caching & Cost Optimization
- •Evaluation Frameworks
- •Safety & Content Filtering
Product
- •AI Product Strategy
- •0→1 Product Development
- •Roadmap Planning
- •User Research & Testing
- •Stakeholder Management
- •Go-to-Market Strategy
- •Agile/Scrum
- •Product Analytics
Technical
- •TypeScript/JavaScript
- •React/Next.js
- •Node.js
- •Python
- •PostgreSQL
- •API Design
- •AWS/Vercel
- •Git/GitHub