Key Skills

Job Description

Senior Machine Learning Scientist | GenAI, LLMs, Applied ML & Model Evaluation €115 per hour – Based on a 40-hour working week 6-month contract Amsterdam (Hybrid) – 2 days per week onsite About the Client Digital Skills are working with a Global Technology Business developing large-scale, customer-facing digital products. The team is building intelligent applications that apply Generative AI and machine learning to multimodal data, with a strong focus on delivering practical solutions into production. About the Role We are looking for a highly hands-on Senior Machine Learning Scientist who can take an ML or GenAI solution from a business requirement through design, experimentation, implementation and evaluation. This is an individual contributor role, with a strong emphasis on writing code and building solutions rather than primarily leading teams or managing stakeholders. The work is highly applied. Research is expected to be targeted and quickly translated into working solutions, rather than focused on long-term academic research. Responsibilities Design and build end-to-end Generative AI and machine learning solutions, including hands-on coding, model development and GenAI pipelines. Develop solutions using techniques including LLMs, NLP, Computer Vision and multimodal machine learning. Build and evaluate RAG and other GenAI pipelines, selecting appropriate approaches for specific product requirements. Define evaluation frameworks and meaningful metrics to rigorously assess ML and GenAI model performance. Conduct targeted applied research and quickly translate findings into practical product solutions. Break complex ML problems into components and determine the appropriate approach for each part of the solution. Consider latency, throughput, cost, scale and performance when selecting models and designing solutions. Desired Skills and Experience Strong hands-on experience developing machine learning solutions end-to-end, including personally writing code and implementing solutions. Strong knowledge of Generative AI and LLM-based applications, with the technical depth to explain how the approaches used actually work. Experience with RAG, LLM evaluation, prompting and/or function calling, with an understanding of the strengths and limitations of different approaches. Strong practical experience defining evaluation methodologies and metrics for ML or GenAI systems. Advanced experience across NLP, Computer Vision and multimodal machine learning. Experience training, fine-tuning and evaluating ML and Generative AI models at scale. Strong Python skills, alongside experience with technologies such as Java, Kafka, Hadoop, SQL and Spark or comparable technologies. Experience working with large datasets, experimentation, statistics, optimisation and data visualisation. MSc with at least 3 years' relevant working experience, PhD with at least 2 years' relevant working experience, or equivalent experience. Summary of the Best Candidate The strongest candidate will be a hands-on Senior Machine Learning Scientist with genuine depth in building applied ML and GenAI products. They should be comfortable taking a business use case, starting with a pragmatic MVP, designing the solution architecture, writing the code, evaluating its effectiveness and considering how it will operate at production scale. Crucially, this is not a role for someone whose recent experience has primarily centred on architecture, team leadership or stakeholder management

Core Responsibilities

Design, code, and evaluate end-to-end machine learning and Generative AI solutions, including LLM, RAG, NLP, computer vision, and multimodal pipelines. Develop evaluation frameworks and translate targeted research into practical product solutions while accounting for latency, throughput, cost, and production scale.

Requirements

Candidates need strong hands-on experience building end-to-end ML and GenAI solutions, with practical expertise in evaluation, RAG, LLM applications, NLP, computer vision, and multimodal machine learning. Strong Python skills and experience with large-scale data, experimentation, and technologies such as Java, Kafka, Hadoop, SQL, or Spark are sought; the posting specifies an MSc plus at least three years of relevant experience, a PhD plus at least two years, or equivalent experience.