Services
ML infrastructure, inference, applied research, and LLM training.
Snapshard designs, builds, and ships production ML systems end-to-end. Engagements span four capability areas, often combined within a single project.
01 / 04
ML infrastructure & platform engineering
Distributed training and serving infrastructure that scales with the work, not the team running it.
Engagement: Multi-week project, or ongoing platform retainer.
What this looks like in practice
- Distributed training pipelines on GPU clusters
- Feature stores and model registries
- Experiment tracking and reproducibility tooling
- CI/CD for model deployment
- Cost monitoring and capacity planning
02 / 04
Optimized inference & deployment
Cut latency, cost, or both. Production serving stacks designed for the scale and reliability your users see.
Engagement: Fixed-scope optimization sprint, typically 4–8 weeks.
What this looks like in practice
- Latency reduction via quantization and distillation
- Serving stack design (Triton, vLLM, TGI)
- Cost-per-token analysis and optimization
- Throughput benchmarking and capacity sizing
- Reliability hardening: autoscaling, fallback, observability
03 / 04
Applied research & R&D
Targeted research that ends in shippable code, not slide decks. Built on a research foundation that knows the difference.
Engagement: Discrete project, typically 6–12 weeks.
What this looks like in practice
- Targeted literature reviews and approach selection
- Custom architecture design for novel constraints
- Reproducing and extending published methods
- Internal benchmarks against the state of the art
- Research-to-production handoff documentation
04 / 04
LLM training & fine-tuning
Domain-specific language models, end-to-end. From data and eval design through SFT, preference optimization, and deployment.
Engagement: Project-based, typically 8–16 weeks depending on scope.
What this looks like in practice
- SFT, RLHF, and DPO training pipelines
- Domain adaptation and continued pretraining
- Synthetic data generation and curation
- Eval harness design tied to real user outcomes
- Inference-aware training (quantization-friendly architectures)
Sound like a fit?
Tell us about the system you're building. A 30-minute call is enough to know if we're the right team.