ML engineering and AI systems advisory
Build models that hold up in production.
fairgradient.ai helps teams design, evaluate, and scale machine learning systems with engineering rigor and enough responsible AI discipline to earn trust.
The work is not to make AI feel inevitable. The work is to make model behavior measurable, systems reliable, and deployment decisions technically defensible.
Practice areas
Advisory for high-stakes AI work.
Engagements are scoped around the engineering question in front of the team: what to build, how to evaluate it, where it breaks, and how it will operate after launch.
ML Systems Advisory
Architecture, evaluation, and scaling guidance for teams building recommendation, ranking, NLP, and agentic AI systems.
Performance & Reliability Audit
Practical assessment of inference cost, latency, monitoring, failure modes, and the operating discipline around production ML.
Model Evaluation Review
Independent review of metrics, datasets, regressions, edge cases, and the evidence used to decide whether a model is ready.
Executive Briefings
Clear, technically grounded briefings for leaders making decisions about AI architecture, delivery, risk, and investment.
Method
Technical depth, made legible.
Inspect the system
Clarify the model, data, architecture, metrics, and delivery constraints.
Find the failure modes
Identify reliability gaps, evaluation blind spots, cost issues, and responsible deployment concerns.
Create the plan
Deliver a concise technical recommendation leaders and builders can act on.
Speaking and evidence
Public work on applied AI.
Talks, interviews, and writing on generative AI systems, production machine learning, evaluation, and responsible deployment.
Podcast feature
Navigating the AI Landscape: Challenges and Innovations in Retail
A discussion of generative AI in retail, personalization, consumer prediction, autonomous shopping agents, and architectural trade-offs at enterprise scale.
Listen NowConference talk
Scaling Large Models with Model and Data Parallelism
Techniques and tradeoffs for scaling large AI models with model and data parallelism.
Watch RecordingIndividual advisory
Career services remain available.
Mock Interviews (ML/AI)
Rigorous technical mock interviews (System Design, Coding, Theory) with detailed feedback.
DetailsCareer Mentorship
1:1 coaching for engineers transitioning into AI or aiming for Staff/Principal roles.
DetailsResume & Portfolio Review
Strategic positioning of your projects and experience to attract top-tier tech companies.
DetailsOffer Negotiation & Comp Strategy
Maximize your total compensation with data-driven negotiation tactics for base, equity, leveling, and competing offers.
DetailsNotes
Recent writing
The Three Horizons of Epistemic Change
AI-generated text is epistemically different from human text, and detection methods face inherent limitations. Three distinct phases emerge as synthetic content accumulates in our information systems over time.
The Noise That Looks Like Signal
If AI-generated content is epistemically different from human content, can't we just detect and filter it? A look at why detection tools face fundamental challenges as language models keep improving.
Your Mistakes Are More Valuable Than You Think
A counterintuitive proposition: one of the most valuable properties of training data is human error — not random error, but the structured, systematic, informative errors Gerd Gigerenzer called 'good errors.'