Applied ML engineers
People who can work across data, model behavior, evaluation, and system-level tradeoffs instead of optimizing in isolation.
Consultant network
We work with independent specialists who want to contribute to client-facing systems where deployment, integration, and clear delivery standards matter.
Who we look for
We care less about titles and more about whether someone can work through practical constraints, communicate clearly, and contribute to systems that have to function in production.
People who can work across data, model behavior, evaluation, and system-level tradeoffs instead of optimizing in isolation.
Consultants comfortable with packaging, monitoring, infrastructure choices, and the friction of getting systems live.
Profiles who understand latency, hardware constraints, reliability, and the realities of running systems outside the data center.
People who bring subject-matter context, data QA discipline, annotation leadership, or workflow knowledge from the field.
How collaboration works
The goal is simple: bring the right people into the right phase of the work without creating unnecessary process or role confusion.
Some contributors join for one well-defined phase. Others stay across feasibility, deployment, and post-launch improvement.
Responsibilities are defined around actual delivery needs, with direct access to project context and decision makers where appropriate.
Work can be remote, hybrid, or occasionally on-site depending on the project, the client, and the operational requirements.
Good fit if
Join the network
Send a short introduction, the kind of work you want to do, and links that help us understand your experience.