machine learning engineer

Digital Waffle develops AI agents designed to perform complex tasks for everyday users, including workflow management and long-term context retention. The company focuses on building reliable, production-ready machine learning systems rather than simple wrappers.

• Build end-to-end pipelines across data, training, evaluation, and inference;
• Adapt and fine-tune models using techniques such as LoRA, QLoRA, SFT, DPO, and distillation;
• Architect inference systems that meet real-world latency and cost constraints;
• Create data pipelines for high-quality synthetic and real-world training data;
• Run evaluations focusing on robustness, safety, bias, and production behavior;
• Own deployment, including GPU optimization, quantization, memory efficiency, and scaling;
• Collaborate with application engineers to integrate ML into backend, mobile, and desktop systems.

• Deep understanding of deep learning and transformer architectures;
• Proven experience training, fine-tuning, or shipping large-scale models in production;
• Proficiency with at least one major ML framework such as PyTorch or JAX;
• Familiarity with distributed training and inference tooling including DeepSpeed, FSDP, Megatron, ZeRO, and Ray;
• Strong engineering discipline with a focus on readable, robust, and maintainable code;
• Experience optimizing for GPU constraints like quantization, mixed precision, and memory;
• Ability to take ownership of ambiguous problems from zero to one;
• Experience shipping, iterating, and learning from production environments;
• Nice to have: LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer), RLHF (PPO, DPO, ORPO), open-source contributions to ML or systems libraries, scientific computing, compiler or GPU kernel experience, multimodal or diffusion model background, large-scale data processing (Arrow, Spark, Ray).

• No conditions specified