Machine Learning Engineer

Machine Learning Engineer, PhD

Aix-en-Provence, France

About this role:

ALPHA10X is the AI leader in private markets investments. Our Nostradamus platform analyzes data at scale to shape the knowledge and foresight that generate alpha and impact. The company is a well-funded growth stage startup and aspiring unicorn, rapidly expanding our revenue, customer base, and market presence, while scaling operations, enhancing products, and securing additional funding to further accelerate development. Our team includes prominent business leaders, investors, scholars, and data scientists.

ALPHA10X is seeking for a highly motivated Machine Learning Engineer (PhD). Reporting to the VP of Data Science, as a ML Engineer you will improve, deploy, and optimize machine learning models that integrate seamlessly with our knowledge graph and investement decision platform and value for our customers. In this role, you will have the opportunity to tackle complex challenges, work autonomously, and contribute to innovative solutions that enhance our platform’s capabilities.

Key Responsibilities:

Model Productionalization and Optimization: Productionalize POC ML models across various domains (e.g., regression, classification, NLP) that are efficient, scalable, and accurate, aligned with business goals.

Data Engineering: Preprocess, clean, and transform large datasets to create reliable and scalable data pipelines, leveraging big data tools such as Apache Spark.

MLOps and Deployment: Deploy models in production environments, ensuring high performance, reliability, and scalability. Implement best practices for MLOps, including versioning, tracking, and monitoring using tools like MLflow and Docker.

Collaboration and Integration: Work closely with cross-functional teams, including data science, software engineering, and product teams, to integrate ML solutions into the broader application ecosystem.

Continuous Improvement: Research and stay updated on emerging ML Ops techniques, applying best practices to optimize models deployments and workflows for production readiness.

What We Value:

• Initiative and curiosity.
• Low ego because the outcome matters more than who gets the credit.
• Adaptive and introspective; willing to learn, guide, lead, and follow.
• Unrelenting drive to push the boundaries of AI and data science.

What we are looking for:

• PhD in Machine Learning, Computer Science, Data, or a related field.
• Machine Learning Expertise, strong grasp of DL/ML algorithms (e.g., GBT, DNN, Transformer, GNN, …) and associated evaluation metrics.
• Proficiency in Python and related ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proven experience with SQL, data preprocessing, feature engineering, and handling large-scale datasets using tools like PySpark on Databricks or equivalent.
• Strong coding practices, including modularization, unit testing, and version control with Git.
• Familiarity with model deployment using MLOps tools such as MLflow, Airflow, or Kubeflow, as well as experience in inference optimization techniques, including model/data slicing for parallel processing.
• Continuous learning mindset.
• Experience with a Software testing framework.
• Experience with LLM and AI agent frameworks (LangChain, LlamaIndex, LangGraph, …) is nice to have.
• Excellent verbal and written English communication skills.
• API framework experience.

Why Join Us?

• Innovation: Play a key role in advancing the capabilities of AI in fintech, working with some of the brightest minds in the industry.
• Leadership: As part of the future shaping team, you will have a direct impact on the company's growth and strategic direction.
• Growth Opportunities: Join a fast-paced, high-growth startup with significant equity participation and potential for personal and professional advancement.
• Global Impact: Be part of a team that connects people, capital, and ideas to solve some of the world’s greatest challenges through AI-driven financial solutions.