AIML - Applied Research Engineer, Machine Translation

Would you like to play a critical part in the next revolution of human-computer interaction? The Apple Machine Translation team is building groundbreaking technology that enables connecting people across language barriers. We are looking for an Applied Research Engineer who is passionate about leveraging the latest advances in large language models and reinforcement learning to create, maintain, and ship scalable, high-quality model assets across a multitude of languages - powering Apple’s Machine Translation products such as the Translate App, Safari web translation, system-wide translation, and Live Translation, powered by Apple Intelligence, Apple’s Machine Translation is deeply embedded across the iOS, iPadOS, macOS, and watchOS ecosystems: from the Translate App that bridges communication across languages, to Live Translation, powered by Apple Intelligence, which enables seamless, real-time translation experiences across calls, messages, and everyday interactions. As LLMs redefine what is possible in natural language understanding and generation, this role sits at the intersection of cutting-edge research and real-world product impact.

You will apply and advance modern training paradigms, including SFT, RL-based fine-tuning, and preference optimization, to push translation quality to new heights across text and speech modalities. You will own and improve end-to-end model development pipelines, from data acquisition and synthetic data generation through training, evaluation, and production rollout.

You will be part of a motivated and dynamic team responsible for shipping models that reach hundreds of millions of users, with a relentless focus on quality, efficiency, and continuous improvement”,”responsibilities”:”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

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, 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