Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
About the role
- Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
Responsibilities
- Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.
- Build scalable, robust data science systems—from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring—meeting enterprise-grade performance, reliability, and compliance standards.
- Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.
- Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.
- Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.
- Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.
- Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).
Requirements
- 3+ years’ experience in Data Science, Machine Learning, or a related field
- BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)
- Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)
- Strong programming skills (Python preferred; Java, Scala, or similar also valued)
- Proven ability to build and optimize scalable data science solutions—end-to-end—from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)
- Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)
- Demonstrated success in translating business needs into analytical solutions, driving quantifiable impact
- Strong stakeholder engagement skills, with a track record of building trusted business partnerships and driving adoption of data science initiatives
- Exceptional ability to simplify and communicate complex data science concepts to technical and non-technical audiences alike
- Experience working cross-functionally with engineers, analysts, and product leaders
- Steadfast commitment to continuous learning, collaboration, and fostering an inclusive, innovative team environment.
Benefits
- Opportunity to develop and scale of LLM and deep learning solutions with real-world business impact
- An environment that values innovation, ownership, and professional growth
- The chance to work on high-visibility, high-impact projects at scale alongside a passionate multidisciplinary team
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