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Apple's Machine Translation team is seeking an Applied Research Engineer to develop groundbreaking technology that enables real-time translation across language barriers. This role sits at the intersection of cutting-edge research and product impact, powering Apple's Translate App, Safari web translation, system-wide translation, and Live Translation features across iOS, iPadOS, macOS, and watchOS.
You will design and implement LLM fine-tuning pipelines using modern training paradigms including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), Group Preference Optimization (GRPO), and related RL-based methods tailored to machine translation quality objectives. You will own end-to-end model development from data acquisition and synthetic data generation through training, evaluation, and production rollout.
Key responsibilities include:
- Designing and implementing LLM fine-tuning pipelines (SFT, RLHF, GRPO, and related RL methods) for machine translation
- Driving production model improvements end-to-end: experimentation, offline evaluation, A/B testing, and customer-facing rollout
- Generating and curating training data (organic and LLM-synthesized) to improve translation quality across text and audio modalities
- Developing and maintaining large-scale distributed training pipelines optimized for rapid iteration
- Building robust tooling for automated quality checks, regression testing, and model benchmarking across language pairs
- Defining evaluation criteria and reward signals reflecting real-world translation quality
- Collaborating cross-functionally on data assets, model versioning, and release schedules
- Staying current with latest research in LLMs, machine translation, and RL-based training methods
You will be part of a motivated team responsible for shipping models reaching hundreds of millions of users, with focus on quality, efficiency, and continuous improvement.
Minimum Qualifications:
- 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 SFT, RLHF, and 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 proactive, collaborative approach to teamwork
- Deep motivation to ship impactful products for Apple's customers
Preferred Qualifications:
- 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
- Distributed and cloud computing experience (GCP, AWS, or equivalent)
- Experience with speech translation or multimodal models
