• Own the roadmap for Vegetation Intelligence, spanning from the ML models that produce our risk data to the products that turn it into customer action. • Push our detection models further, and help fold their outputs into Overstory’s broader outage and ignition risk models. • Contribute towards increasing the number of actionable insights: run user research, ground priorities in the operational decisions customers actually make, and make sure what we ship drives real action in the field. • Shift the roadmap from scaling- and technical-led to customer-driven, without losing the technical rigor that got us here. • Work closely with data scientists and ML engineers to turn model capability into shipped product value, and define what “good” looks like for a model in production. • Explore how feedback from customers and end users, like arborists, can improve model performance over time. • Communicate clearly and often with the broader product and engineering team, so trade-offs, model limits, and priorities stay visible. • Contribute to Overstory’s broader product strategy so Vegetation Insights supports where the rest of the product is headed

• Own the full ML lifecycle from data collection to ongoing quality assessment. • Train CV/ML models to detect deepfakes and uncover complex anomaly patterns. • Balance research and speed in model building and heuristic solutions. • Serve models efficiently with FastAPI and PyTorch, integrating with Java backend. • Build and monitor ML pipelines and dashboards with tools like Airflow and ClickHouse.

Role Description - Design, develop, and deploy end-to-end ML and AI pipelines, from problem framing and experimentation through to production. - Build and maintain robust data and ML pipelines for model training, evaluation, and deployment, develop LLM-powered features including RAG, structured prompting, few-shot learning, and OCR integration. - Implement and evolve ML/LLMOps practices: model versioning, production monitoring, reproducibility, and continuous improvement. - Develop APIs and microservices to expose ML/AI models to business applications. - Analyze large datasets, identify modeling challenges, propose solutions, and iterate quickly. - Collaborate closely with Data, Product, and Engineering teams to turn prototypes into scalable, reliable production solutions. - Stay current on academic and industrial advances in Machine Learning, LLMs and emerging AI technologies and apply them to real-world use cases. - Contribute to best practices in testing, CI/CD, and deployment automation. Qualifications - You hold a Master's degree (or equivalent) in Computer Science, Applied Mathematics, Data Science, or a related quantitative field. - You have 3 to 5 years of experience developing and deploying Machine Learning models in production environments. - You are proficient in Python and have hands-on experience with major ML/AI frameworks (scikit-learn, XGBoost, LightGBM, HuggingFace Transformers, DSPy…), valuing practical experience and adaptability over specific tools. - You have solid knowledge of ML/LLMOps tools (MLflow, Kubeflow, DVC, DeepEval or similar) and ML-oriented DevOps practices. - You have experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) and containerization (Docker, Kubernetes). - You are comfortable with SQL and knowledgeable in feature engineering and data preprocessing best practices. - You have a pragmatic, analytical, and results-oriented mindset: favoring frequent iterative deliveries over long research tunnels. - You enjoy working in cross-functional teams (Data, Tech, Product) and can translate business needs into concrete ML/AI solutions. - You are autonomous, curious, and comfortable in a fast-evolving ecosystem. - Professional proficiency in English, bilingual in French. Requirements - Package: 55.000 Euro to 70.000 Euro - fix salary only. Benefits - Join a mission-driven company with a concrete impact on the new generation. - Share our three core virtues with our people all over Europe: #Committed, #OneTeam, #DrivEn. - Take part in a dynamic internal life, where everyone can contribute (all-hands, local parties, team drinks, team-buildings, etc.). - Grow and develop your career with a strong training policy, including an annual individual budget for learning opportunities (95% of our employees were trained last year!). - Enjoy our flexible work environment: remote-friendly, with one month per year from anywhere in Europe, and home office equipment support. Some positions can even be fully remote. - Benefit from our Family Care Policy: 4 additional paid days off during your return month from parental leave, 3 paid child-sick days per year, and other initiatives to support parents. - Take care of your mental health with Moka Care: 4 free therapy or coaching sessions per year (with additional support in challenging personal situations), plus regular workshops for managers in partnership with Moka Care and Alan. - Make your voice count: our Take Care Survey (run twice a year) ensures employee feedback directly shapes JobTeaser’s culture, collaboration and cross-team work. - Protect your focus time with our No-Meeting Wednesday mornings initiative. - Collaborate closely with our CSE, actively involved in enhancing work life and benefits (with the HappyPal platform). - Enjoy the essentials: RTT, health insurance, lunch vouchers, holiday bonus, and 50% public transport reimbursement. - A modern and well-located office at WeWork - including delicious barista coffee and free yoga lessons. - Be proud: 88% of our employees say they are proud to work in their team at JobTeaser (Internal Take Care Survey, November 2024).

• Design, build, and deploy AI/ML solutions with a focus on large language models (LLMs) using Python and AWS infrastructure. • Architect scalable APIs and microservices utilizing FastAPI, ensuring robust, high-performance systems. • Integrate LangChain and advanced AI frameworks to deliver innovative model interaction capabilities. • Collaborate with cross-functional teams to translate complex mathematical, statistical, or physics-based problem statements into actionable engineering solutions. • Apply core math, statistics, or physics knowledge to improve model performance, interpretability, and reliability. • Maintain high standards of code quality, documentation, and peer reviews within a fast-paced core team. • Communicate technical concepts and project progress clearly through both written reports and verbal discussions.