AI/ML Engineer
Nelnet
AWSBigQueryCloudDockerGoogle Cloud PlatformJavaScriptKubernetesMongoDBNeo4jNode.jsPySparkPython+3 more
Principal ML Engineer – Personalisation
Beyond
contract
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteJob Level
Lead
Tech Stack
AWSCloudDistributed SystemsDockerGoogle Cloud PlatformGraphQLKubernetesPythonPyTorchScikit-LearnTensorflow
About the role
- Lead the architecture and evolution of scalable, high-performance personalisation backend systems, including the ingestion and processing of data to create AI driven personalization pipelines.
- Drive cross-functional initiatives to establish industry leading personalization technologies, leveraging traditional and advanced Machine Learning models, as well as large language models (LLM) to improve user experience through personalisation.
- Define strategies to enhance the performance, reliability, and observability of personalization services, ensuring low-latency, high-availability systems.
- Design and implement frameworks for evaluating personalisation quality through both offline metrics and live A/B experimentation.
- Champion engineering best practices and mentor engineers across teams, raising the bar for code quality and system design.
- Shape long-term technical direction by staying ahead of trends in personalisation and recommendation technologies, distributed systems, and bringing these innovations into production.
Requirements
- Degree in Computer Science, Engineering, or a related technical field.
- 8+ years of experience designing and leading the development of large-scale distributed backend systems.
- Hands-on experience with personalization infrastructure and/or recommendation engines is a strong advantage.
- Deep expertise in Collaborative Filtering (user-item, item-item), Content-Based Filtering, and Matrix Factorization techniques (SVD, ALS).
- Experience developing advanced models, such as:
- - Deep Learning Architectures: Including Two-Tower models for scalable candidate retrieval, and sequence-aware models like Transformers or RNNs for session-based recommendations.
- - Hybrid Models: Combining multiple approaches (e.g., collaborative and content-based) to overcome their individual limitations.
- Developing specialized Techniques, such as:
- - Multi-Armed Bandits (for exploration vs. exploitation)
- - Learning to Rank (LTR) for optimizing ordered lists
- - Generating embeddings for users and items
- Mastery of Python and its core ML ecosystem, including TensorFlow, PyTorch, Scikit-learn, and XGBoost.
- Demonstrable experience building robust APIs (REST, GraphQL) and operating in modern cloud environments (GCP, AWS), using Kubernetes, Docker, CI/CD, and observability tools.
- Proven ability to lead and influence engineering direction across teams and functions.
- Strong communication skills and the ability to align diverse technical stakeholders around a cohesive vision.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
personalization backend systemsmachine learning modelslarge language modelsCollaborative FilteringContent-Based FilteringMatrix FactorizationDeep Learning ArchitecturesHybrid ModelsMulti-Armed BanditsLearning to Rank
Soft skills
mentoringengineering best practicesleadershipinfluencingcommunication
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