Lead Machine Learning Engineer

Recruiting a Lead Machine Learning Engineer requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.
(Reporting to the Chief Machine Learning Engineer)

The Lead Machine Learning Engineer is a senior technical expert specializing in the design, optimization, and deployment of machine learning systems. While an individual contributor, they play a key role in industrializing ML models and mentoring more junior ML engineers. They work closely with product teams (who manage backlogs) and data scientists to transform analytical models into robust, scalable, and maintainable production solutions, while ensuring knowledge transfer and best practices within the team.

Responsibilities and Missions

1. Develop and Optimize ML Systems for Production

Design and implement complete MLOps pipelines (from training to serving).
Optimize model performance in production (latency, throughput, memory).
Develop infrastructure solutions for large-scale deployment.
Automate testing and monitoring of production models.

2. Collaborate with Product and Data Science Teams

Work with Product Owners to understand technical requirements.
Translate analytical models into industrializable technical solutions.
Participate in prioritization meetings to align work with backlogs.
Provide realistic technical estimates for planning.

3. Mentor and Guide Junior ML Engineers

Guide juniors in designing and implementing ML solutions.
Review code and architectures proposed by the team.
Organize training sessions on MLOps best practices.
Help solve complex technical problems.

4. Ensure Quality and Reliability of Systems

Implement automated tests for ML pipelines.
Define and apply quality standards for code and infrastructure.
Document technical solutions and processes.
Ensure security and compliance of systems (GDPR, etc.).

5. Innovate and Improve Existing Processes

Evaluate new technologies (frameworks, tools, architectures).
Propose improvements to existing ML pipelines.
Lead technical PoCs to validate new approaches.
Participate in technological watch and knowledge sharing.

6. Contribute to the Team’s Technical Culture

Promote ML engineering best practices.
Participate in code reviews and technical discussions.
Document solutions and processes for knowledge sharing.
Contribute to continuous improvement of technical standards.

Examples of Concrete Achievements

Optimized a serving pipeline, reducing latency by 40% while improving stability.
Mentored 3 junior ML engineers, improving their MLOps skills by 30%.
Developed a monitoring system for production models, reducing anomaly detection times by 50%.
Automated the feature engineering pipeline, reducing processing times by 35%.
Implemented an A/B testing solution for models, improving deployment accuracy by 20%.

Required Skills and Qualities

Technical expertise in ML engineering (MLOps, distributed architectures, optimization).
Proficiency in ML frameworks (TensorFlow, PyTorch, scikit-learn) and DevOps tools.
Experience with production systems (Kubernetes, Docker, cloud platforms).
Ability to mentor juniors and share technical knowledge.
Rigor in code quality and documentation.
Innovation mindset and strong problem-solving skills.