I build products end to end, from first idea to production.
Full-lifecycle software engineering, machine learning, LLM tooling and agent harnesses, and the project management that gets it all shipped.
At LinkedIn, I built the forecasting model behind a nine-figure hardware allocation.
How I Work
Most consultants give you a strategy deck. Most engineers give you code without context. I do both: I understand your business problem, architect a solution, and build it myself. From cloud infrastructure to ML pipelines to the AI layer on top, I work across the full stack so nothing gets lost in translation.
End-to-End Systems Thinking
I don't just build features. I design systems. Whether it's a data pipeline, a microservices architecture, or an AI workflow, I think about how the pieces fit together, how they scale, and how they fail gracefully.
Working Software, Fast
No 6-week discovery phases. I prototype early so we're debating a running system instead of a slide deck, then harden what works with tests, observability, and documentation. You see evidence before you commit.
Built for You to Own
I don't create dependency. You'll understand how it works, how to maintain it, and how to extend it. My goal is to make myself unnecessary, or to stick around because you want me to.
What I Do
The technical depth of a senior engineer with the strategic perspective of a CTO. Here's how we can work together:
AI & Machine Learning Engineering
From proof-of-concept to production. I design and build ML systems that actually work: RAG pipelines, fine-tuned models, agentic workflows, and the infrastructure to support them. Not just prompts and API calls, but real engineering.
Software Engineering & Architecture
I write production code. Backend systems, APIs, data pipelines, cloud infrastructure. I built and shipped ArcScore Inc.'s platform end to end, and I did research and engineering at Adobe, IBM, AMD, and LinkedIn. When you need something built right, I build it.
Fractional CTO / Technical Leadership
Need senior technical leadership without the full-time hire? I work with startups and growing companies as a fractional CTO: setting technical direction, making build-vs-buy decisions, leading engineering teams, and getting my hands dirty when needed.
Technical Cofounder (Selective)
For the right opportunity, I partner with non-technical founders as a technical cofounder. I bring the engineering, you bring the vision. Let's talk if you're building something meaningful.
Temporary Team Extension
Need another senior engineer for a stretch? I embed with your team to work through engineering problems: your repo, your CI, your code review. When the crunch is over, I hand off cleanly and get out of the way.
What this looks like in practice
The thread through my work: ML applied to the economics of expensive, constrained resources. Each story is labeled with the role I did it in, and claims are limited to what the public record supports.
NIL Valuation Platform
As founding engineer (contract) at ArcScore Inc.
ArcScore had a rough MVP and no production system: no pipeline, no infrastructure, and no defensible way to put a number on an athlete's NIL value.
I architected and built the platform end to end (a Next.js/React front end, a FastAPI back end, and a provider-agnostic Python data pipeline) and designed the core scoring and NIL-valuation methodology with two other modeling leads. I provisioned and now operate the production infrastructure: managed Postgres, a job queue, auth, secrets, CI/CD, and separate staging and production environments with observability on both.
Live production platform. I own significant portions of the application, data, infrastructure, and and engineering roadmap.
Capacity Planning Model
As an applied research intern at LinkedIn
The Applied Research team faced a confidential nine-figure hardware-allocation forecasting problem across LinkedIn's data centers.
I built a predictive model for it. The implementation is confidential, so the specifics stay with LinkedIn.
13.39% improvement over the existing methodology.
Accelerating Physics Simulations
As a scientific ML research intern at AMD Research
Physics-based simulations at AMD were expensive to run, and an ML surrogate is only useful if you can tell when to stop trusting it.
I worked on a hybrid method that interleaves physics-based simulation with neural network training and inference, and on Bayesian and ensemble uncertainty-quantification methods that flag unreliable surrogate predictions on the fly.
The method is public: I'm a named inventor on AMD's patent filing US 2025/0005236 A1 and a co-author of a NeurIPS 2022 ML4PS workshop paper. The filing describes the hybrid workflow completing the simulation in less time than the baseline. It states no speedup factor, so neither do I.
Infrastructure for Coaches
As founder of TechnicallyFit, my own company
A coach can only take on as many clients as they can program, check in on, and answer questions for by hand.
I built TechnicallyFit's platform so that work runs as software: a program engine, coach workspaces, and a cited sports-science knowledge base that the AI coach and outside AI assistants can query over MCP.
Infrastructure that lets coaches scale their businesses, and the foundation for TechnicallyFit's larger goal: the operating system for human performance.
How We Work Together
A clear path from first conversation to a production system your team owns.
Discovery & Architecture
We dive deep into your technical challenges. You get detailed architecture diagrams, tech stack recommendations, and a clear implementation roadmap.
Strategy & Planning
We refine the approach together: priorities, timelines, resources. You know exactly what's being built and why.
Build & Deploy
I build your production systems. Real infrastructure, real code, real results you can measure and scale.
Handoff & Training
You take ownership. I provide documentation, runbooks, and training until your team can maintain and extend it independently.
Ready to get started?
Let's talk through your situation and see if there's real value to deliver.
Technical & academic coaching
Paid one-on-one sessions on ML systems, architecture, research, and PhD or career questions.
Hey, I'm Darian.
I'm a machine learning researcher and a systems builder, not just an AI consultant. I earned my PhD in Computer Science at Cornell, defending in July 2026 with the degree conferred that August, and I did research at Adobe, IBM, AMD, and LinkedIn along the way. I'm a named inventor on AMD's patent filing on accelerating physics-based simulations with AI. But what I actually do is build things that work.
Most people in AI either give you high-level advice or write code without understanding your business. I sit in the middle. I can whiteboard your system architecture, write the code myself, deploy it to production, and explain to your investors why it matters. Since October 2025 I've been doing exactly that as founding engineer at ArcScore Inc.: I architected and built the platform end to end, designed the core scoring and valuation methodology with two other modeling leads, and I own significant portions of the application, data, infrastructure, and roadmap.
The thread running through my work is ML applied to the economics of expensive, constrained resources: forecasting a nine-figure hardware allocation at LinkedIn, accelerating physics-based simulations at AMD, pricing athlete value at ArcScore. If your hard problem lives where models meet infrastructure, that's my lane.
I take on consulting, fractional technical leadership, and selective founding work through this site. I'm also open to the right full-time research or senior engineering role; my resumes and full CV are on my work page.
- Machine learning research and production software engineering
- Research roles at Adobe, IBM, AMD, and LinkedIn
- PhD in Computer Science, Cornell, 2026
- Named inventor on AMD patent filing US 2025/0005236 A1
- I write code. I ship products. I lead teams.