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Bespoke Labs Tech Stack
AI data curation and post-training infrastructure for LLMs
Bespoke Labs builds infrastructure for preparing and curating training data for large language models, with a focus on reinforcement learning for agents. The tech stack reveals a heavy emphasis on orchestration (Kubernetes, Airflow, Spark) and ML ops tooling (MLflow, Weights & Biases, PyTorch, TensorFlow), indicating they're solving for distributed data processing and model training at scale. Active hiring is almost entirely data-focused (25 of 46 roles), paired with senior-level engineering, suggesting the company is scaling production systems for high-throughput data pipelines rather than expanding product surface area.
Tech Stack 53 technologies
Core StackKubernetes Terraform GitLab CI/CD Go Python Java Docker gRPC Prometheus Grafana NumPy Pandas scikit-learn PyTorch TensorFlow Apache Spark PySpark MLflow Apache Airflow Weights & Biases AWS TypeScript Node.js Linux Keycloak Kaggle Spark SQL SQL GCP Azure+23 more
What Bespoke Labs Is Building
◆Challenges
- Scaling data processing for ai training
- Ensuring data platform reliability
- Curating ai training data at scale
- Low latency data processing
- Scaling issues
- Secure deployments
- Outages
- Improving model performance
- Distributed systems challenges
- System performance
▲Active Projects
- Ai training data curation
- Benchmark task creation from devops incidents
- Custom ingestion logic
- Secure scalable kubernetes-native architectures
- Distributed workflows and compute orchestration
- Core backend systems for ai training
- Algorithm design for data quality evaluation
- High-throughput data processing system
- Kubernetes-native architecture development
- Benchmark tasks from real incidents
Hiring Activity
45 roles · 10 in 30d
Department
Seniority
Company intelligence
