Giotto.ai

Systems Engineer

• Own the end-to-end post-training pipeline from pretrained checkpoint to production candidate
• Design and execute full-parameter and parameter-efficient SFT
• Implement preference optimisation, RLHF, RLAIF, reinforcement learning with verifiable rewards, and related methods
• Develop training strategies for reasoning, coding, tool use, multilingual behaviour, and long-horizon agent tasks
• Integrate reward models, verifiers, critics, graders, and process- or outcome-based rewards
• Build scalable rollout-generation systems for iterative and on-policy training
• Design multi-stage curricula combining SFT, reinforcement learning, rejection sampling, distillation, and policy consolidation
• Scale training across multiple machines and accelerators using appropriate combinations of data, tensor, pipeline, sequence, context, or expert parallelism
• Select sharding, precision, checkpointing, optimiser, batch-size, sequence-length, and activation-recomputation strategies
• Estimate memory, communication, throughput, rollout capacity, and compute requirements before launching major runs
• Profile and improve accelerator utilisation, communication efficiency, data loading, and end-to-end training time
• Diagnose numerical instability, communication failures, out-of-memory errors, stragglers, checkpoint issues, and convergence regressions
• Investigate reward hacking, entropy collapse, KL drift, stale rollouts, mode collapse, grader exploitation, and benchmark overfitting
• Build reliable checkpointing, recovery, monitoring, and reproducibility procedures
• Collaborate closely with data, evaluation, infrastructure, and inference teams
• Contribute clean, tested code, technical reports, and operational runbooks

Senior Research Engineer / Research Scientist – Post-Training, Reinforcement Learning & Training Systems