Software Engineer- Training Product

Details

• Overview of the product so far
• Training docs overview
• Story of the Training product
• Research we've done
• Checkpointing Pipeline: Our checkpointing pipeline starts with automated checkpointing, a feature that ensures that versions of models created during training are automatically backed up to the cloud. Users are able to then deploy checkpoints seamlessly into inference servers, providing point-and-click integrations into inference frameworks like vLLM and Baseten’s Inference Stack. This enables customers to quickly evaluate the performance of their checkpoints with real traffic.
• Multinode training: Multinode training enables customers to easily run training jobs across multiple compute nodes, enabling users to train large models like GLM 4.7 and DeepSeek. We’ve built deeply at the Kubernetes layer to ensure that scheduling, startup, inter-node communication, and shutdown happen seamlessly under the hood and as the user expects.
• Training DX: Customers come to train on Baseten because it helps them get to value fast. To do this, we ensure that the features we ship aren’t just fast, but are easy to iterate with. We enhanced Baseten’s metrics from pod-level GPU summaries to per-GPU and per-Node. We’ve built a CLI experience that caters to terminal users, and UI experiences that enable user to seamlessly manage their training jobs.
• Iterate like crazy
• Design ergonomic APIs and abstractions to model complex resources and lifecycles
• Work throughout the stack (API layer, backend and database implementation, infra layer; frontend is a plus) to implement features.
• Fine-tune and deploy models to develop intuition around training workflows.
• Partner closely with model developers and world-class research engineers to understand the requirements and pain points of post-training workflows.
• Drive long-term improvements to improve reliability of systems and velocity of development
• Fix bugs & resolve customer issues with urgency
• 5+ years experience building software applications
• Deep knowledge of the web stack, databases, and distributed systems
• Experience developing developer tooling or infrastructure products for external or internal users.
• Good taste in product, particularly developer-oriented tools
• Interest in ML/AI infrastructure and willingness to learn
• Driven by high agency and ownership
• Strong communication skills with the ability to bridge technical depth and business needs
• Experience launching features and products through different release cycles (MVP, Beta, GA, etc.)
• Experience with model development methods and paradigms, like Supervised Fine-Tuning, Reinforcement Learning, Synthetic Data Generation, LoRA, Full Finetunes, etc.
• Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainer) and distributed training techniques (FSDP, DeepSpeed).
• Experience developing AI products, tooling, or agents
• Frontend fluency
• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Fertility and family-building stipend through Carrot
• Company-facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.