Bjak - Principal Machine Learning Engineer

• Strong background in deep learning and transformer-based architectures. • Hands-on experience training, fine-tuning, or deploying large-scale ML models in production. • Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly. • Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray). • Strong software engineering fundamentals – you write robust, maintainable, production-grade systems. • Experience with GPU optimization, including memory efficiency, quantization, and mixed precision. • Comfort owning ambiguous, zero-to-one ML systems end-to-end. • A bias toward shipping, learning fast, and improving systems through iteration. • Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer. • Contributions to open-source ML or systems libraries. • Background in scientific computing, compilers, or GPU kernels. • Experience with RLHF pipelines (PPO, DPO, ORPO). • Experience training or deploying multimodal or diffusion models. • Experience with large-scale data processing (Apache Arrow, Spark, Ray). • How We Work • How We Work • The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product