Team Lead Machine Learning Engineer
Zurich
Auf einen Blick
- Veröffentlicht:15 Oktober 2025
- Pensum:100%
- Vertragsart:Festanstellung
- Sprache:Englisch (Fliessend)
- Arbeitsort:Zurich
About Constructor Knowledge
Constructor Knowledge AG, formerly known as Constructor Operations AG or Schaffhausen Institute of Technology Operations AG, is a wholly owned subsidiary of Constructor Group AG and an integral part of the broader Constructor ecosystem (“Constructor Group”) — a global, research-driven institution dedicated to fostering innovation and addressing some of the world’s most pressing challenges.
Constructor Group brings together experts across diverse fields—spanning science, technology, business, and management—to develop transformative solutions through interdisciplinary collaboration.
Within this ecosystem, Constructor Knowledge AG plays a central role by providing shared services in Switzerland that support various entities and projects across the network. These services include research and development, legal, finance, operations, marketing, human resources, and communications. By delivering these essential capabilities, Constructor Knowledge AG ensures the efficient and effective functioning of the entire ecosystem.
One of the strategic projects supported by Constructor Knowledge AG is Inita — an initiative focused on transforming cutting-edge AI research into reliable, revenue-generating products. Inita’s intelligent AI handles the tedious parts, allowing entrepreneurs to focus on what truly matters: growing their business. To accelerate this mission, we’re looking for a Team Lead Machine Learning Engineer to join the team and help drive the development of our next generation of AI-powered solutions.
Key Responsibilities
- Design, develop, and deploy advanced ML models, with a focus on optimizing Large Language Models (LLMs) for diverse NLP and multimodal applications (text, image, structured data) and Visual Language Models (VLM)
- Build and manage CI/CD pipelines and oversee the full model lifecycle, neurocomputer ng robust, production-ready ML systems.
- Continuously evaluate and integrate state-of-the-art research into Inita’s AI stack to keep the company at the forefront of innovation, manage large data pipelines.
- Contribute to cross-functional strategy and product development, translating advanced AI research into practical, revenue-generating solutions.
- Lead AI projects, ensuring all ML components are aligned with business objectives, communicate with the stakeholders and leadership team
- Coordinate closely with backend, infrastructure, and product teams for the strategic integration of AI into core products.
- Provide technical leadership and mentorship to a team of ML engineers, ensuring knowledge transfer, training, and capability building.
Required Skills & Expertise
- Proven track record in applied ML (7+ years) with advanced expertise in deep learning, NLP, computer vision, Visual Language Model and LLM optimization.
- Specialized expertise in building multimodal AI systems that integrate and analyse structured, text, and image data.
- Strong programming and deployment skills in Python, TensorFlow, PyTorch, Docker, Flask, SQL,
- Tech leadership experience managing ML engineering teams and complex projects, including in fast-moving startups where agility and speed are critical.
- Team Lead experience
- Exceptional background in Computer Science, Data Science, Mathematical Modelling or equivalent.;
- Excellent English communication skills (written and spoken); other languages is a plus
Nice to have
- Programming and deployment skills in AWS
- Teaching experience and/or academic group project lead experience in applied/computational mathematics and/or computer science
- Strong foundation in Classical Machine Learning and Deep Learning methods
- Experience with Generative AI and production-grade web services
What we offer
- An attractive job in a start-up environment with a competitive salary package
- Opportunity to be part of an international team
- Opportunity to contribute to and participate in the organization of regular events