AgentGraph pairs enterprises with vetted AI engineers to design and ship agents, MCP servers, and agentic systems. Builders join the network and are matched to client engagements that fit their expertise and working style.
Machine Learning Engineers are the modeling specialists in the network. Where an engagement needs a model built, trained, deployed, and kept healthy in production — rather than a frontier model called through an API — this is the role that owns it.
What you will do
- Build and deploy machine learning models across supervised, unsupervised, and reinforcement learning approaches, choosing the technique the problem actually calls for
- Own the data pipelines that feed training and inference — collection, labeling strategy, feature engineering, versioning, and the unglamorous data quality work that determines whether any of it works
- Run models in production: monitoring, drift detection, retraining cadence, and scaling to the throughput and latency the client's workload demands
- Work on small language models and model distillation — right-sizing models so that quality, latency, and cost land where the client needs them, rather than defaulting to the largest model available
- Partner with Forward Deployed and Applied AI Engineers on engagements where a custom model is one component of a larger agentic system
What we look for
- Production ML experience end to end — not just training models, but deploying them, watching them degrade, and fixing them
- Fluency with the tradeoffs between classical ML, fine-tuned open-weight models, and hosted frontier models, and the evidence to back a recommendation
- Hands-on work with distillation, quantization, or other model right-sizing techniques under real cost and latency constraints
- Rigor about evaluation: golden datasets, offline and online metrics, and regression detection that catches a bad model before it ships
How engagements work
You join the AgentGraph network as an independent contractor rather than an employee. We match you to client engagements based on your expertise, availability, and the shape of the work; scope, rate, and expected commitment are agreed before an engagement starts. Work is remote, and engagements range from short scoped projects to sustained embedded work with a single client.
Compensation
Rates are set per engagement, based on scope, depth of expertise, and expected commitment. We are transparent about the rate before you accept any engagement.