Applied Research Engineer, Machine Translation at Apple

Aachen, North Rhine-Westphalia, Germany -

Full Time

Start Date

Immediate

Expiry Date

12 Jul, 26

Salary

0.0

Posted On

13 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C++, Machine Learning, Natural Language Processing, Large Language Models, Supervised Fine-Tuning, Reinforcement Learning, RLHF, PyTorch, Data Processing, Spark, Dask, Synthetic Data Generation, Sequence-to-Sequence Models, Software Engineering, Distributed Computing

Industry

Computers and Electronics Manufacturing

Description

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 DESCRIPTION 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 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 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 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 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

Responsibilities

You will develop and optimize machine translation models using advanced training paradigms like SFT and RLHF to enhance translation quality across Apple products. You will also own the end-to-end model development pipeline, covering everything from data acquisition to production deployment.