Aktuelle Jobs im Zusammenhang mit Data ML Engineering Lead - zürich - Kaiko
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AI Engineering Lead: Scalable ML
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Zürich, Schweiz KPMG Schweiz VollzeitA leading consulting firm in Zürich is seeking an Engineering AI Team Lead to ensure robust and scalable AI solutions. In this role, you will blend leadership and hands-on engineering, mentoring engineers and data scientists. You will define technical standards and oversee the development of AI solutions while promoting best practices and operational...
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Lead Data Engineer
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Senior Data Scientist
vor 1 Woche
Zürich, Schweiz Acquism SARL VollzeitA leading tech company in Switzerland is seeking a Senior Data Scientist to lead high-impact AI/ML projects. The role involves defining business problems, delivering solutions using Google Cloud, and mentoring teams. Ideal candidates will have over 7 years of experience in data science, advanced Python skills, and strong ML toolkit mastery. This contract...
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Tech Lead AI Umfeld(GenAI, ML, GCP)
Vor 7 Tagen
Zürich, Zürich, Schweiz PROSTAFF Schweiz GmbH VollzeitUnser Auftraggeber (Versicherungsbranche) baut aktuell ein GenAI-Engineering-Team mit drei Spezialist:innen auf. Bereits an Bord sind ein Junior/Professional sowie ein fest angestellter Professional. Für die nächste Ausbauphase suchen wir einen erfahrenen Contractor (Senior GenAI Engineer), der hands-on entwickelt und zugleich als Tech Lead die technische...
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ML Data Engineer
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Cloud Data
vor 2 Wochen
Zürich, Schweiz SIX VollzeitWhat You Will Do be part of an international team that envisions and implements the roadmap of a mature cloud native Data Science Platform, currently serving more than 14 analytics teams and 400+ users. closely collaborate with key stakeholders from business data science teams and IT data Engineering teams to enable their capabilities on the DSP: Identify...
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Freelance Data
vor 3 Wochen
Zürich, Schweiz Lightly AG VollzeitLightly AG is an ETH and HSG spin-off pioneering in machine learning and computer vision. Backed by Y Combinator and top-tier investors, our technology is used by global leaders in autonomous driving, medical imaging, and visual inspection. With a small but sharp team in Zurich, we’re looking for analytical and driven Data & ML Experts to help us translate...
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Freelance Data
vor 2 Wochen
Zürich, Schweiz Lightly AG VollzeitLightly AG is an ETH and HSG spin-off pioneering in machine learning and computer vision. Backed by Y Combinator and top-tier investors, our technology is used by global leaders in autonomous driving, medical imaging, and visual inspection. With a small but sharp team in Zurich, we’re looking for analytical and driven Data & ML Experts to help us translate...
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Engineering Manager
vor 2 Wochen
Zürich, Schweiz KPMG Switzerland VollzeitA leading consulting firm in Switzerland is seeking an Engineering Manager - AI Services and Solutions to lead the technical delivery of AI solutions. The role involves mentoring engineers, defining architectural standards, and managing project delivery. Candidates should have over 5 years of experience in AI/ML engineering, strong programming skills in...
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Zürich, Schweiz Decentriq VollzeitA rising leader in data-clean-room technology is seeking a Senior Software Engineer specializing in distributed systems to enhance analytics and ML pipelines. The role involves ownership of data pipelines, collaboration with cross-functional teams, and the application of Python, pandas, and Spark to advance ML models. Ideal candidates hold a...
Data ML Engineering Lead
vor 2 Wochen
About kaiko Delivering high quality cancer care is complex; specialists form a view of each patient's condition by reasoning across different data - CT scans, genomics context, treatment history and clinical notes. Current AI are powerful within domains but fall short when it comes to reasoning across data or domain areas. kaiko.w, our AI assistant for oncology, aims to equip every clinician with a full understanding of their patients, helping them to reason across data as they assess each case. We're building this in close collaboration with the Netherlands Cancer Institute (NKI) and a growing network of hospitals and research centers. We've raised significant long-term funding and have nearly doubled our team over the past year. We're now 80+ people representing 25 nationalities, based across our offices in Zurich and Amsterdam About the role Kaiko's Multimodal Large Language Model (MLLM) is trained on domain-specific, high-complexity medical data. To reach clinical-grade performance, we'll need to ramp up our data efforts to manage massive scale, ensure consistent quality, and tightly control data relevance and integrity. As our ML Data Engineering Lead, you'll directly drive this frontier. You'll own a significant portion of our data roadmap and steer the technical direction of our ingestion, generation, and filtering pipelines. You'll work closely with the ML research lead to align infrastructure with research priorities, design scalable architectures, and prioritize experiments that increase our data leverage. This is both a managerial and hands-on position, which means you'll drive technical execution on core data systems while coaching your team & enabling your reports to grow. You will be based in Zurich or Amsterdam, with the expectation of spending at least 3 days a week at the office. About you You've led technical teams before: hiring, coaching, and developing engineers to do their best work. You've owned technical direction for large-scale data pipelines, making long-term architectural decisions around ingestion, synthetic data generation, and filtering that aligned with company and research priorities. You have deep experience designing and operating large-scale data pipelines,including synthetic data generation for LLMs, data-quality experimentation, and core data primitives like hashing, deduplication, and chunking. You still have excellent programming skills in Python and deep experience with distributed frameworks such as Ray or Spark. Nice to have: Hands‑on production experience orchestrating complex DAGs in Dagster (preferred) or similar workflow engines. Expertise in data‑quality & validation frameworks and monitoring/observability tooling. Strong grasp of machine‑learning fundamentals (model architectures, training paradigms, evaluation metrics) to collaborate deeply with researchers and guide data‑driven choices. We are excited to gather a broad range of perspectives in our team, as we believe it will help us build better products to support a broader set of people. If you're excited about us but don't fit every single qualification, we still encourage you to apply: we've had incredible team members join us who didn't check every box Why kaikoAt kaiko, we believe the best ideas come from collaboration, ownership and ambition. We've built a team of international experts where your work has direct impact. Here's what we value: Ownership: You'll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work. Collaboration: You'll have to approach disagreement with curiosity, build on common ground and create solutions together. Ambition: You'll be surrounded by people who set high standards for themselves and others, who see obstacles as opportunities, and who are relentless in their work to create better outcomes for patients. In addition, we offer: An attractive and competitive salary, a good pension plan and 25 vacation days per year. Great offsites and team events to strengthen the team and celebrate successes together. A EUR 1000 learning and development budget to help you grow. Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings. An annual commuting subsidy. Our interview process Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps (though it will be more extensive for leadership positions): Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role. Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario. Onsite meeting: You'll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context. Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.
