Senior Machine Learning Engineer job opportunity at Qodea.

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Date2025-11-25T16:15:27.428Z bot
Qodea Senior Machine Learning Engineer
Experience: General
Pattern: full-time
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degreeAssociate
loacation London, United Kingdom
loacation London....United Kingdom

Work where work matters. Elevate your career at Qodea, where innovation isn't just a buzzword, it's in our DNA. We are a global technology group built for what's next, offering high calibre professionals the platform for high stakes work, the kind of work that defines an entire career. When you join us, you're not just taking on projects, you're solving problems that don't even have answers yet. You will join the exclusive roster of talent that global leaders, including Google, Snap, Diageo, PayPal, and Jaguar Land Rover call when deadlines seem impossible, when others have already tried and failed, and when the solution absolutely has to work.

 Forget routine consultancy. You will operate where technology, design, and human behaviour meet to deliver tangible outcomes, fast. This is work that leaves a mark, work you’ll be proud to tell your friends about.

 Qodea is built for what’s next. An environment where your skills will evolve at the frontier of innovation and AI, ensuring continuous growth and development.

 We are looking for a Senior Machine Learning Engineer to be responsible for the end-to-end lifecycle of machine learning models that power core product features. You will design, build, and deploy innovative ML solutions, directly impacting the user experience through personalization, recommendations, and intelligent systems. We look for people who embody: Innovation to solve the hardest problems. ‍ Accountability for every result. ‍ Integrity always. About The Role Lead the algorithm selection, design, and prototyping of machine learning models to solve complex business problems, including recommendation, personalization, and predictive analytics. Apply your expertise in statistical modeling and machine learning to perform deep data analysis, guide crucial feature selection, and identify opportunities for product improvement. Own the full ML lifecycle, from breaking down discrete steps of a pipeline (e.g., with a DAG) to analyzing model implementations and improving their robustness in the wild. Implement and manage robust model observability, tuning, and optimization processes to ensure sustained performance and accuracy post-deployment. Develop and maintain data pipelines to process and prepare data for model training and evaluation. Design and conduct A/B tests to evaluate model performance and its impact on key business metrics. Collaborate closely with product managers and engineers to define problems and deliver effective AI-driven solutions. Mentor other team members, champion best practices in machine learning engineering, and stay current with the latest advancements in the field. This role is designed for impact, and we believe our best work happens when we connect. While we operate a flexible model, we expect you to spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops. What Success Looks Like Hands-on experience designing and deploying production-grade machine learning systems. Strong foundational knowledge of various machine learning algorithms and a proven ability to select the appropriate methodology, avoiding a one-size-fits-all approach. Proven experience in areas such as recommendation systems, personalization, natural language processing (NLP), or semantic search. Expert-level programming skills in Python, with deep, hands-on experience using data science and ML libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch. Experience with data storage technologies (e.g., SQL, NoSQL, Key-value) and their scaling characteristics. Experience with large-scale data processing technologies (e.g., Spark, Beam, Flink) and associated patterns (Batch vs. Stream), with a deep understanding of when to use them. Experience using cloud platforms (e.g., GCP) at scale. Experience deploying ML-based solutions at scale using cloud-native services. Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment.

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