Lead Machine Learning Engineer

Das ist der Job

ppDataHow AG is a spin‑off company of ETH Zurich founded in 2017.

Darum lohnt es sich

You will work closely with our Algo/RD team, bringing their algorithms into robust, production‑grade software and contributing an engineering perspective to their discussions. /p pThis is an engineering role, not a research position: the focus is on building, productionising, and scaling ML systems reliably, and on partnering effectively with research rather than driving it. /p h3Leadership /h3 ul liOwning our MLOps practice end to end: defining and enforcing standards for benchmarking, model versioning, reproducible training pipelines, and reliable deployment /li liDriving ML/model‑serving backend architecture, including model wrapping, training pipelines and MLOps services /li liManaging team members through regular 1:1s and supporting their professional development /li liDriving hiring for the team, from screening and technical interviews to coordinating with external recruiters /li liMentoring junior engineers and data scientists, and raising the quality bar across the team through consistent, thorough code review /li liActing 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 /li liProductionising and improving machine learning models and algorithms in Python and PyTorch, owning the full lifecycle from prototype to production deployment and stakeholder approval /li liPartnering with the Algo/RD 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 /li liMaintaining and improving our data transformation and model training pipelines, as well as internally maintained packages, SDKs, and services used by internal and external users /li liKeeping the codebase healthy: rapidly diagnosing and fixing bugs, keeping dependencies up to date, and reducing technical debt through tooling modernization /li /ul h3Requirements /h3 ul liM.Sc. or higher in Computer Science, Data Science, or a related field /li li5+ years of professional experience in machine learning or software engineering, including 3+ years developing and deploying ML models in production systems /li liPrior experience in a formal lead or people management role.

Demonstrated experience mentoring engineers and conducting rigorous code reviews /li liStrong understanding of machine learning concepts and algorithms, in particular the combination of time‑series forecasting and differentiable systems /li liExperience designing microservice architectures and making or strongly influencing architecture decisions; solid working knowledge of Docker /li liProficiency in programming languages commonly used in data science, such as Python or Julia, and libraries/frameworks (e.g., PyTorch, scikit‑learn, JAX) /li liDeep familiarity with MLOps practices including experiment tracking, model versioning, reproducible training pipelines, and automated retraining workflows /li liExperience writing production‑quality, well‑tested Python code /li liExcellent problem‑solving skills and strong attention to detail /li liStrong written and verbal communication skills, with the confidence to present to customers and to challenge ideas constructively at all levels of the organization /li /ul h3Nice to Have /h3 ul liExperience developing novel ML algorithms, e.g. combining time‑series forecasting with differentiable systems /li liExperience working in the biopharma/bioprocessing domain /li liExperience in customer‑facing project work /li liExperience with Go and/or Rust /li liPh.D. or peer‑reviewed publications in machine learning or a related field /li /ul h3We Offer /h3 ul liA high degree of autonomy and direct influence on product and technical direction /li liHybrid working environment (home and office) /li liAbility to visit other international offices, join customer visits, and attend international conferences /li liPossibility to spend 10% of your work hours on a work‑relevant personal research project /li liSharp learning and responsibility curve in a challenging and interdisciplinary working environment /li liAttractive working conditions and progression in our company /li /ul h3Pay range and compensation package /h3 pIntegration into a young, dynamic, and interdisciplinary team of computationally driven chemical engineers, biotechnologists, and computer scientists.

Hybrid work model, with flexibility between our main office in Lisbon and home office. A steep learning curve, significant responsibility, and exposure to high‑impact international projects. Our mission is to bring Industry 4.0 to the chemical, pharmaceutical, and biopharmaceutical sectors.

We develop and commercialise predictive software that integrates sensor signals, process models, machine learning, reinforcement learning, historical data, and process engineering knowledge.

Our technology accelerates process development, transfer, manufacturing, and risk quality management—already trusted by 8 of the world’s 20 largest pharma companies. /p h3About the Role /h3 pWe 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.

A highly versatile role balancing advanced research, real-world industrial applications, and customer interaction. Opportunities for travel, conferences, and participation in company‑wide community events.

Attractive working conditions and clear opportunities for career progression. /p h3Equal Opportunity Statement /h3 pDataHow AG is committed to diversity and inclusivity in the workplace. We encourage applications from individuals of all backgrounds and experiences. /p /p #J-18808-Ljbffr