Machine Learning Engineer - 52268

About Turing: Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L. Responsibilities • Design, implement, and maintain large-scale RLHF (Reinforcement Learning from Human Feedback) and RLAIF pipelines. • Train and deploy reward models and policy optimizers (e.g., PPO, DPO, or GRPO) for aligning LLMs. • Work with human feedback and data teams to process and integrate preference datasets. • Develop evaluation and monitoring systems for model alignment, stability, and safety. • Optimize distributed RL training for efficiency, scalability, and reproducibility. • Collaborate with research scientists to translate experimental algorithms into robust production code. • Contribute to internal tools, open-source libraries, and research publications. Required: • Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field • 2+ years of experience in reinforcement learning, deep learning, or applied ML. • Proficiency in Python and frameworks like PyTorch, JAX, or TensorFlow. • Experience with distributed training, scalable data pipelines, and GPU/TPU optimization. • Familiarity with LLMs, transformer architectures, and fine-tuning methods. Preferred: • Experience implementing or scaling RLHF pipelines (reward modeling, preference collection, policy optimization). • Experience working with large-scale model training (multi-node, mixed precision). • Familiarity with alignment, safety, or interpretability research. • Contributions to open-source ML frameworks or published research in RL / NLP.