- Lead and manage a team of 5-10 machine learning, applied ML, and ML platform engineers building production-grade ML systems, platforms, and services for Cognite Data Fusion, a state-of-the-art SaaS platform for industrial data.

- Partner closely with product managers, designers, data engineers, and platform teams to translate business and user problems into scalable ML solutions that deliver measurable impact.

- Own the end-to-end ML lifecycle : problem formulation, data exploration, feature engineering, model selection, training, evaluation, deployment, monitoring, and iteration, supported by scalable ML platform capabilities.

- Coach and mentor engineers with diverse backgrounds and levels of seniority, helping them grow in applied ML, software engineering best practices, and domain understanding.

- Guide the team in applying ML techniques pragmatically, balancing model performance, system reliability, scalability, and maintainability.

- Actively contribute to the technical design and architecture of ML systems and ML platforms, including model-serving infrastructure, feature stores, data pipelines, experimentation frameworks, and shared ML tooling.

- Remove blockers for the team by collaborating with stakeholders across engineering, data, platform, and business functions.

- Set and evolve the ML product and technical roadmap in alignment with broader product strategy.

- Stay hands-on when needed : reviewing ML code, model designs, experiments, and documentation, and occasionally contributing code or prototypes.

- Champion a strong culture of experimentation, data-driven decision-making, continuous improvement, and platform reuse across ML teams.

- Use and evaluate our own products (dogfooding) to deeply understand user workflows and identify opportunities for applied ML enhancements.

Requirements :

- Proven experience working in a product-focused technology company.

- 2+ years of people management experience, leading ML, applied ML, or data science teams.

- 10+ years of overall engineering experience, with at least 4+ years in applied machine learning, ML platforms, or data-intensive systems.

- Strong foundation in machine learning concepts, including supervised and unsupervised learning, model evaluation, and feature engineering.

- Hands-on experience deploying and operating ML models in production environments, ideally via shared ML platforms or self-serve MLOps tooling.

- Proficiency in one or more programming languages commonly used in applied ML systems, such as Python, Java, Go, Scala, or similar.

- Solid understanding of end-to-end system design and architecture for ML-powered applications.

- Experience building and scaling systems that process large volumes of data.

- Familiarity with MLOps and ML platform practices such as CI/CD for ML, model monitoring, feature management, retraining strategies, experimentation platforms, and developer enablement.

- Prior experience as a senior engineer, ML tech lead, or staff engineer is a strong advantage.

- Strong interest in developing engineering talent and building high-performing ML teams through empathetic leadership.

- Collaborative mindset with the ability to align teams around shared goals and trade-offs.

- Experience owning and delivering complex features end-to-end in a fast-paced environment.

- High motivation to work in an environment with evolving requirements and significant technical challenges.