• Design and implement LLM fine-tuning pipelines (SFT, RLHF, GRPO, and related RL-based methods) tailored to machine translation quality objectives
• Drive production model improvements end-to-end: from experimentation and offline evaluation through A/B testing and customer-facing rollout
• Generate and curate training data — both organic and LLM-synthesized — to improve translation quality across text and audio input modalities and accelerate expansion into new languages
• Develop and maintain large-scale distributed training pipelines optimized for rapid iteration and reproducibility
• Build robust tooling for automated quality checks, regression testing, and model benchmarking across existing and new language pairs
• Define and track evaluation criteria and reward signals that reflect real-world translation quality, enabling data-driven decisions on model releases
• Collaborate cross-functionally to manage data assets, model versioning, and release schedules across a growing portfolio of languages and platforms
• Stay current with the latest research in LLMs, MT, and RL-based training methods, and rapidly prototype and integrate promising advances into production workflows

• Strong programming and software engineering skills (Python, C++, or equivalent), with hands-on experience training and fine-tuning large-scale models
• Experience building and optimizing machine translation, natural language processing, or related sequence-to-sequence systems using modern LLM architectures
• Practical knowledge of LLM post-training techniques, including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Group Preference Optimization (GRPO) or similar reward-based optimization methods
• Experience with large-scale data processing frameworks (Spark, Dask, or equivalent) and synthetic data generation pipelines
• Strong production mindset: ability to take models from research to reliable, customer-facing deployment
• Ability to manage complex processes across multiple stakeholders in a fast-paced environment
• Excellent communication skills and a proactive, collaborative approach to teamwork
• Deep motivation to ship the best, most impactful products for Apple's customers

• Master’s degree or PhD in Computer Science, Electrical and Computer Engineering, or related field
• Experience in applied machine learning or software engineering, with demonstrable impact on shipped products or systems
• Hands-on experience with deep learning frameworks (PyTorch or equivalent) and large-scale model training
• Familiarity with reward modeling, preference data collection, or RL-based fine-tuning for language models is a strong plus
• Distributed and cloud computing experience (GCP, AWS, or equivalent) is a plus
• Experience with speech translation or multimodal models is a plus

At Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced, and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. We will work with applicants to make any reasonable accommodations.