Applied Research - RL & Agents
Tasks
- Architect and maintain distributed training and inference pipelines
- Build evaluations to measure reasoning robustness and agent behavior
- Design and implement reinforcement learning and post training methods
- Design and iterate AI agents for real workloads
- Develop AI agent infrastructure for reliable scalable operation
- Develop observability and monitoring for production reliability and performance
- Experiment with post training recipes for downstream performance
- Prototype agents and evaluation harnesses for real world tasks
- Prototype multi-agent and memory-augmented systems
- Translate ambiguous objectives into technical requirements
Perks/Benefits
- Conference attendance
- Flexible work
- Professional development budget
- Relocation support
- Team offsites
- Visa sponsorship
Skills/Tech-stack
Accelerate | Agent Frameworks | Distributed Training | Distributed inference | Docker | Dspy | Evaluation | Grafana | Kubernetes | Langgraph | Language Models | Large Language Models | Machine Learning | Model Alignment | Next.js | Observability | Prometheus | PyTorch | RLHF | Ray | React | Reinforcement Learning | Terraform | Torch | Tracing | TypeScript | VLLM
Education
N/A
Regions
Countries
States
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