Senior Staff Engineer at Stryker bridging R&D and Manufacturing. Northwestern mpd² candidate building fluency in the fuzzy front end of innovation.
I serve as the bridge between Research and Development and Manufacturing — evaluating global supplier capabilities, driving supply chain readiness, and guiding contract manufacturers through production approval while balancing cost, quality, and schedule. Six years of launching connected medical devices, from first-generation Wi-Fi hospital stretchers to global manufacturing transfers across Turkey, China, and Taiwan, have taught me that the best product decisions live at the intersection of technical depth and business clarity.
I traded capital goods for disposables, Kalamazoo for Chicago, and a familiar business unit for a new portfolio — because growth requires the right stimulus. I am pursuing Northwestern's Master of Product Design and Development Management to strengthen my approach to the fuzzy front end of innovation: defining user needs, building financial models that inform strategy, and developing the leadership vocabulary to move from executing launches to shaping product roadmaps.
I came into mpd² as an engineer who knew how to launch things. Six years of NPI at Stryker taught me that if you get the process right — PPAP, supplier readiness, tooling, validation — the product ships. What I couldn't always do was explain why this product over another, or why now instead of next quarter, in language that moved a room.
Year one changed the altitude. Not reinvention — expansion. I still bridge R&D and Manufacturing. I still launch things. But now I can frame the opportunity upstream, defend it financially, ground it in evidence, and carry people along through the decision.
What started as curiosity about local LLMs in December became Token Jockey, then LLM Launcher, then Chi-Muter — each one a step from tinkering toward building real product systems with specs, evals, and users in mind. AI Jetpacks formalized the shift: from using chat tools to designing prompts, retrieval pipelines, evals, and repeatable workflows as infrastructure around real work.
That's what Year Two is for — doing it for real, at a higher altitude, with sharper wedges.
Promoted to Senior Staff. Continuing to lead surgical positioning device manufacturing transfer and supply chain strategy while expanding scope across the SAGE Advanced Operations portfolio.
Led surgical positioning device launch with 50% COGS reduction through China manufacturing transfer. Guided contract manufacturer through first PPAP while managing 26-month supply plan across committed, negotiation, and auto-adjusted order horizons.
Led Prime Connect launch — first Wi-Fi-connected hospital stretcher generating $21M in first-year sales. Recovered critical supplier timeline through on-site Shenzhen visits, proving that technical communication can transcend language barriers.
Managed $0.75M CapEx budget. Developed internal torque specification capability across 60+ tools. Built COVID Emergency Relief Bed production cells under rapidly changing conditions, deepening an empathy for operators that now shapes every process I design.
Part-time program alongside full-time role. Coursework in sustainable design, materials selection, life cycle assessment, and product strategy. Bringing a Voice of Operations perspective to a cohort of seasoned product leaders from diverse industries.
Biomedical Engineering concentration. Alumni Distinguished Scholar, Honors College. GPA: 3.95/4.0.
DFMA, Process Validation (IQ/OQ/PQ), PPAP, SCADA, SPC, DMAIC, Injection Molding, PCBA Integration, Python, Data Visualization
Make vs. Buy Analysis, NPV/IRR Modeling, CapEx Management, Supplier Negotiation, Tariff Strategy, Product Roadmapping
Cross-Functional Teams, Global Coordination (China, Turkey, Taiwan), Operator Training, Stakeholder Management, Crisis Recovery
A multi-modal commute companion for Chicago transit
Chi-Muter is a connection-protection layer for recurring commuters who coordinate across CTA buses, CTA rail, and Metra. Unlike Google Maps, Transit App, or Ventra — each of which solves one slice of the trip — Chi-Muter is focused on the handoff between transit systems. It evaluates live CTA arrivals against rigid Metra departures, calculates transfer makeability, and tells you whether your connection chain is still intact. The MVP runs on a Raspberry Pi backend over Tailscale, fusing CTA Bus Tracker, CTA Train Tracker, and Metra GTFS feeds into a single managed commute view.
Saved commute templates instead of one-time trip search. Multi-leg connection health evaluation with green/yellow/red status. Compares feeder options (9 vs. X9) against a Metra anchor departure. Leave-by guidance and buffer-aware recommendations.
Raspberry Pi backend running as a systemd service. CTA Bus Tracker and Metra schedule/realtime API integration. Server-side API key handling. Tailscale Serve for secure private access. Mobile-first dashboard with morning/return commute toggle and demo mode for offline review.
Native mobile client for the LLM Launcher stack
A lightweight SwiftUI app for managing and chatting with LLM models — local llama.cpp over Tailscale, plus MiniMax and GLM/ZhipuAI cloud providers. Token Jockey connects to the LLM Launcher server for full model control, and adds a native chat experience that works across local and cloud backends — streaming responses via SSE, storing conversations locally, and supporting per-model system prompts.
Switch between Local llama.cpp, MiniMax, and GLM/ZhipuAI from Settings. All providers use the same OpenAI-compatible SSE streaming path. Native SwiftUI chat with stop generation, markdown rendering, double-tap message navigation, and long-press send to select system prompt (None, Global, Model, or Global + Model).
Local JSON-backed conversation history with swipe-to-delete. Per-provider API keys and model fields stored in iOS Keychain. Chat Provider picker in Settings. Appearance mode and accent color customization. Global and per-model system prompts with active prompt indicators (* model, + global).
The control plane behind Token Jockey
A Python-based remote control server for managing local llama.cpp models with API key authentication, real-time GPU and VRAM monitoring, health checks, and live log tailing — all accessible over Tailscale from anywhere on the mesh. LLM Launcher is the server-side foundation that Token Jockey connects to: it handles model lifecycle (start, stop, swap), exposes an OpenAI-compatible chat endpoint, and serves an embedded web UI for configuration. This was the first piece of the stack — built to solve the practical problem of running inference on a home GPU while working from a laptop across the house or on the road.
External JSON config for model management. Auto-detects Tailscale IP. Binds llama-server to all interfaces for network access. CORS-aware with API key authentication on all endpoints.
Real-time GPU stats (VRAM, utilization, temperature). Health checks that confirm model readiness for inference, not just process liveness. Live log tailing piped from llama-server stdout. Full REST API for external tool integration.
During a recent new product introduction, I built this demand scenario modeler to help leadership visualize inventory and cost implications of demand uncertainty across a 26-month planning horizon. Scroll to adjust the demand scenario from 50–100% of baseline — or drag the slider directly.
NPI Case Study — Contract Manufacturer Supply Planning
Open to conversations about product development, manufacturing engineering, AI-assisted tooling, and the mpd² experience.