Experienced Research Engineer

Storm3; Paris; Hybride; EUR 60 000 - 100 000; Plein temps

Résumé du poste

A leading company in AI is seeking an LLM Engineer with a focus on speech processing to join their team in Paris. The role involves designing and maintaining data pipelines, supporting model training, and collaborating with research teams to optimize AI systems. Ideal candidates will have an MSc or PhD in Machine Learning or Computer Science, and experience in deep learning frameworks. This position offers opportunities to work at the forefront of AI technology in a hybrid environment.

Qualifications

• Proficient in data collection, cleaning, and transformation at scale
• Practical experience with web scraping and large-scale model training
• Exceptional problem-solving and troubleshooting skills

Responsabilités

• Design and maintain scalable video data pipelines
• Support training of multimodal foundation models
• Contribute to codebase maintenance and documentation

Connaissances

Data collection

Data transformation

Model training

Problem-solving

Deep learning frameworks

Communication

Formation

MSc or PhD in Machine Learning or Computer Science

Outils

PyTorch

Scrapy

Selenium

BeautifulSoup

Description du poste

LLM Engineer w / particular focus on speech processing and integration Hybrid in Paris Competitive base The Mission To ttackle the fundamental challenges of world modeling and establish a new paradigm for next-generation machine reasoning . They are looking for passionate individuals who share our vision and are eager to push the boundaries of AI together. Key Responsibilities : Data Infrastructure & Pipelines Design, implement, and maintain scalable video data pipelines to support large-scale training. Develop data preprocessing, transformation, and synthesis workflows to support world model training. Contribute to building high-quality data annotation pipelines to ensure accurate and consistent labels across large-scale datasets. Key Responsibilities : Training & Inference Systems Support the training of multimodal foundation models (e.g., video diffusion models, world models) by developing and optimizing distributed training systems. Improve inference and serving efficiency for real-time interaction through model optimization and system tuning. Monitor system health and performance, and contribute to debugging and optimization at scale. Key Responsibilities : Collaboration & Integration Work closely with research teams to understand experimental goals and translate ideas into reliable and maintainable infrastructure and tools. Integrate novel research prototypes into production-ready systems and ensure reproducibility at scale. Participate in design and code reviews, ensuring code quality, efficiency, and compliance with best practices. Key Responsibilities : Benchmarking & Evaluation Contribute to the development of tools and infrastructure to evaluate model performance using rigorous quantitative benchmarks, including metrics for physical accuracy and controllability. Key Responsibilities : Codebase & Documentation Maintain and extend shared codebases, contribute to internal documentation, and support onboarding of new team members or collaborators. Write clean, efficient, and well-tested code for components across the model development lifecycle. Key Responsibilities Support contributions to research papers and demos when engineering work plays a significant role. Help represent the team’s engineering excellence in internal and external forums when appropriate. Academic Qualifications MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience. Professional Experience Required Proficient in data collection, cleaning, and transformation at scale, including designing robust pipelines for multimodal datasets (e.g., video, audio, text). Practical experience with web scraping and crawling frameworks (e.g., scrapy, selenium, playwright, BeautifulSoup) to collect and curate high-quality web-scale datasets. Experience in large-scale model training (LLMs or Diffusion Models) on large clusters. Experiences in building and optimizing large-scale video data pipelines. Experience in accelerating diffusion model inference for improved efficiency. Exceptional problem-solving and troubleshooting skills to tackle complex technical challenges. Strong systems and engineering expertise in deep learning frameworks such as PyTorch. Strong communication and collaboration skills for effective cross-functional teamwork. Demonstrated ability to solve complex system-level challenges and debug failures across the training / inference stack (e.g., memory issues, deadlocks, I / O bottlenecks).

Research Engineer • Paris, Paris (75); Ile-de-France, France