AI Platform Engineer
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Requirements
- Strong backend and systems engineering fundamentals
- Experience with containerization and orchestration
- Cloud infrastructure experience (AWS/GCP/Modal/Together.ai etc)
- Experience with distillation and compression
- Experience building internal ML platforms
Responsibilities
- Training pipelines
- Fine-tuning workflows
- RL infrastructure
- Data ingestion and curation systems
- Inference services
- Scalability and backend architecture
- You will own the platform that turns models into production systems.
- What You’ll Work On:
- Architect distributed fine-tuning pipelines for small encoder and decoder models
- Implement LoRA, adapters, distillation, and compression workflows
- Design experiment tracking, reproducibility, and dataset versioning systems
- Optimize training efficiency (GPU utilization, memory, throughput, cost)
- Design scalable RL training workflows (policy optimization, reward modeling)
- Integrate RL with supervised fine-tuning and distillation
- Build evaluation loops and automated regression detection
- Build scalable ingestion pipelines for structured and unstructured data
- Design dataset curation, filtering, and quality enforcement systems
- Implement reproducible data workflows tied to training runs
- Architect low-latency inference services
- Design safe production deployment workflows
- Strong candidates will have:
- Deep experience with PyTorch and transformer architectures
- Experience building production ML systems end-to-end
- Experience with distributed training and inference
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