machine learning engineer for AI agents

Digital Waffle is building an AI agent that performs genuine tasks for everyday users, including running errands, managing workflows, and maintaining context across long and complex conversations. The company focuses on reliable AI products where the ML layer is the core product.

• Build end-to-end pipelines across data, training, evaluation, and inference;
• Adapt and fine-tune models using LoRA, QLoRA, SFT, DPO, and distillation;
• Architect inference systems under real latency and cost constraints;
• Create pipelines for high-quality synthetic and real-world training data;
• Run evaluations covering robustness, safety, bias, and production behaviour;
• Own deployment, including GPU optimisation, quantisation, memory efficiency, and scaling;
• Work directly with application engineers to integrate ML into backend, mobile, and desktop applications;
• Own the ML layer and ambiguous problems from zero to one;
• Ship, iterate, and learn from production.

• Deep understanding of deep learning and transformer architectures;
• Proven experience training, fine-tuning, or shipping large-scale models in production;
• Strong knowledge of 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;
• Engineering discipline with readable, robust, and maintainable code;
• Experience optimising for GPU constraints, including quantisation, mixed precision, and memory;
• Ability to take ownership of ambiguous problems from zero to one;
• Nice to have: LLM inference frameworks such as vLLM, TensorRT-LLM, and FasterTransformer, RLHF with PPO, DPO, or 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 with Arrow, Spark, or Ray.

• Remote role within the European Union.