Rivac LabsLLM Post-Training Engineer
When a general-purpose model isn't good enough at your specific domain task, the fix is often post-training — SFT, DPO, GRPO, or RLVR — not more prompt engineering. We run domain-specific post-training with evidence-based experiment gating, meaning every checkpoint is evaluated against real criteria before it's promoted, and checkpoint integrity is protected so a bad run doesn't overwrite a good one. This is for teams with a domain-specific task important enough to justify custom training, and with a real dataset or reward signal to train against. You get a model checkpoint that measurably outperforms the base model on your task, with the eval evidence to prove it.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Define the domain task precisely and assess whether existing data or reward signal is sufficient to train against.
- 2
Run a pilot training pass on a smaller model or dataset slice to validate the approach before scaling up.
- 3
Review checkpoint evaluation results against baseline and gate promotion on evidence, not on-paper progress.
- 4
Scale to the full training run with checkpoint versioning and integrity checks protecting the promoted model.
What You Get
Deliverables from this engagement
- Post-trained model checkpoint outperforming baseline on your task
- Evaluation report comparing checkpoints against defined criteria
- Training pipeline and configuration for future retraining
- Checkpoint versioning and integrity verification setup
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about LLM Post-Training Engineer