Staff Machine Learning Engineer

Responsibilities:

• Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence.
• Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries.
• Drive data- and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, auto-generated tagging solutions, and RAGs.
• Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions.
• Strategically prioritise initiatives and technical workstreams to deliver the highest- impact and most time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution.
• Champion engineering best practices across code quality, testing, CI/CD, observability, and incident response.
• Mentor and coach engineers, fostering a culture of ownership, collaboration, and continuous improvement.
• Contribute to technical documentation and promote knowledge sharing across teams.

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Statistics, Math, or a comparable field of study, and/or equivalent work experience.
• 8+ years of experience building and operating ML engineering systems in production environments.
• Expertise in data science, deep learning algorithms, or statistical methods to solve real-world engineering problems.
• Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training, and low-latency online serving.
• Experience designing and developing backend microservices for large-scale distributed systems using REST.
• Experience with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalise).
• Familiarity with developing and deploying Spark and ML pipelines.
• Hands-on experience with big data technologies such as Databricks, Kinesis, Kafka.
• Proven leadership, coaching, and mentoring skills, with the ability to inspire and empower a team towards achieving business goals.
• Experience with observability tools for metrics, logging, and monitoring such as Datadog.
• Experience working in Agile/Scrum development environments.
• Excellent communication skills and a commitment to collaboration in a fast-paced, guest- focused environment.