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Reflection AI Tech Stack
Open-source AI model development and inference infrastructure
Reflection AI is building foundational AI infrastructure—training pipelines, post-training tooling, and inference systems—with a team of engineers and researchers from DeepMind, OpenAI, and Anthropic. The stack reveals heavy investment in distributed training (PyTorch, Megatron-LM, DeepSpeed, CUDA) and inference optimization (vLLM, SGLang, Triton), both actively being adopted, while the project list (red-teaming, automated QA, RL/SFT tooling, safety benchmarks) signals a focus on making frontier-grade model development accessible beyond closed labs.
Tech Stack 55 technologies
Core StackAshby Python TypeScript Docker Kubernetes PyTorch Rippling RAG Tableau Apache Spark React FastAPI Go gRPC LinkedIn Recruiter CUDA NCCL DeepSpeed JAX Megatron-LM SGLang vLLM Slurm CI/CD SQL Excel RLHF Ray Beam Triton+25 more
AdoptingvLLM SGLang
What Reflection AI Is Building
◆Challenges
- Scaling technical organization
- Reducing operational burden
- Reducing friction in fine-tuning process
- Establishing external partnerships
- Ensuring high data quality
- Hard-to-fill ai research roles
- Reliability at scale
- Cluster utilization and cost efficiency
- Building new systems rather than maintaining legacy ones
- Model factual accuracy
▲Active Projects
- Reusable qa pipelines for post-training data
- Post-training and inference ecosystem
- Red-teaming evaluation pipeline
- Automated qa methods for large data campaigns
- Large-scale gpu infrastructure powering pre-training, post-training, and inference
- Core software systems for research, training, and production environments
- Apis, sdks, and internal platforms for rapid experimentation
- Training infrastructure optimization
- Automated safety benchmarks
- Rl and sft tooling
Hiring Activity
Steady100 roles · 30 in 30d
Department
Seniority
Notable leadership hires: Product Policy Lead, Alignment Lead, Safety Lead, Open Source Lead, Brand Lead
Company intelligence
