Lead Machine Learning Engineer

We are looking for a Lead Machine Learning Engineer to own the quality, evolution, and technical direction of the machine learning stack at the core of our AutoML platform, DataHowLab. This is a hands-on leadership position: you will set the technical bar, drive architecture and MLOps decisions, manage and grow engineers, and act as the technical counterpart to product and management, while remaining deeply hands-on in the code. You will work closely with our Algo/R&D team, bringing their algorithms into robust, production-grade software and contributing an engineering perspective to their discussions.

Key Responsibilities

Leadership

• Owning our MLOps practice end to end: defining and enforcing standards for benchmarking, model versioning, reproducible training pipelines, and reliable deployment
• Driving ML/model-serving backend architecture, including model wrapping, training pipelines and MLOps services
• Managing team members through regular 1:1s and supporting their professional development
• Driving hiring for the team, from screening and technical interviews to coordinating with external recruiters
• Mentoring junior engineers and data scientists, and raising the quality bar across the team through consistent, thorough code review
• Acting as a technical sparring partner for product and management: pressure-testing mockups, feature ideas, and roadmap decisions for feasibility and consistency before they reach implementation

Hands-On Engineering

• Productionizing and improving machine learning models and algorithms in Python and PyTorch, owning the full lifecycle from prototype to production deployment and stakeholder approval
• Partnering with the Algo/R&D team to bring novel algorithms for multivariate time-series prediction and data analytics into production, and contributing an engineering and feasibility perspective to research discussions
• Maintaining and improving our data transformation and model training pipelines, as well as internally maintained packages, SDKs, and services used by internal and external users
• Keeping the codebase healthy: rapidly diagnosing and fixing bugs, keeping dependencies up to date, and reducing technical debt through tooling modernization

• M.Sc. or higher in Computer Science, Data Science, or a related field
• 5+ years of professional experience in machine learning or software engineering, including 3+ years developing and deploying ML models in production systems
• Prior experience in a formal lead or people management role. Demonstrated experience mentoring engineers and conducting rigorous code reviews
• Strong understanding of machine learning concepts and algorithms, in particular the combination of time-series forecasting and differentiable systems
• Experience designing microservice architectures and making or strongly influencing architecture decisions; solid working knowledge of Docker
• Proficiency in programming languages commonly used in data science, such as Python or Julia, and libraries/frameworks (e.g., PyTorch, scikit-learn, JAX)
• Deep familiarity with MLOps practices including experiment tracking, model versioning, reproducible training pipelines, and automated retraining workflows
• Experience writing production-quality, well-tested Python code
• Excellent problem-solving skills and strong attention to detail
• Strong written and verbal communication skills, with the confidence to present to customers and to challenge ideas constructively at all levels of the organization

Nice to Have

• Experience developing novel ML algorithms, e.g. combining time-series forecasting with differentiable systems
• Experience working in the biopharma/bioprocessing domain
• Experience in customer-facing project work
• Experience with Go and/or Rust
• Ph.D. or peer-reviewed publications in machine learning or a related field

• A high degree of autonomy and direct influence on product and technical direction
• Hybrid working environment (home and office)
• Ability to visit other international offices, join customer visits, and attend international conferences
• Possibility to spend 10% of your work hours on a work-relevant personal research project
• Sharp learning and responsibility curve in a challenging and interdisciplinary working environment
• Attractive working conditions and progression in our company