AI / ML Engineer

Description de l'emploi

About the job

Department: Engineering

Employment Type: Permanent - Full Time

Location: Paris

Description

FamilyWall helps manage your Family's everyday life by sharing everyone's schedules and activities, tracking grocery lists, planning for dinner, managing to-dos as well as locating kids when they are outside. With FamilyWall, the whole family is on the same page!

We are seeking a highly motivated AI/ML Engineer - ideally a recent PhD graduate - to focus on the optimization and continuous improvement of AI models, particularly Large Language Models (LLMs). This role emphasizes model evaluation, fine-tuning techniques, dataset quality, and architecture experimentation to improve model performance.

You will work on high-impact AI initiatives that shape the future of user-facing features and internal intelligence tools, with a focus on rigorous evaluation, training iteration, and the pursuit of better architectures and outcomes.

What You Will Accomplish

Model Development & Optimization

• Lead the full lifecycle of AI/ML models, focusing on performance improvements through fine-tuning, architectural experimentation, and continuous refinement.
• Develop and maintain datasets for training and evaluation using real-world and synthetic generation methods.
• Apply grounding strategies and fine-tune LLMs and vision models to enhance factual accuracy and reduce hallucinations.
• Explore new modeling approaches and training methodologies that advance system capabilities.
  
  

• Design and maintain evaluation pipelines to systematically assess model quality across relevance, correctness, robustness, and user impact.
• Conduct detailed error analysis and performance tracking to identify areas for continuous improvement.
• Automate evaluation gates in CI/CD pipelines and support learning loops through strategic annotation and data sampling.
  
  

• Collaborate with Product and Data teams to align modeling goals with product outcomes and application needs.
• Develop internal tools and utilities to support model experimentation, benchmarking, and validation processes.
• Instrument applications for observability and help monitor model behavior in production environments.
  
  

• A researcher-practitioner with a strong foundation in training, fine-tuning, and evaluating modern AI/ML models.
• Focused on improving model performance through thoughtful experimentation, evaluation rigor, and high-quality data.
• Collaborative, communicative, and motivated to solve real-world problems with cutting-edge techniques.
• Capable of supporting engineering efforts where neededespecially those that enable better model evaluation and iteration.
  
  

• PhD in Computer Science, Machine Learning, NLP, or a related fieldrecent graduates are strongly encouraged to apply.
• Strong understanding of LLMs, fine-tuning methods (LoRA, PEFT, RLHF), and evaluation frameworks (LLM-as-a-judge, inter-annotator agreement, etc.).
• Experience with Python and ML libraries such as PyTorch, HuggingFace Transformers, and vLLM.
• Hands-on experience with dataset curation, training loop design, and architecture search.
• Familiarity with tools such as Weights & Biases, MLflow, or LangChain for tracking and analysis.
• Engineering experience (e.g., API exposure, model deployment) is a plus but not a strict requirement.