Senior Software Engineer - Distributed Systems (MLOps)

myGwork
Edinburgh
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Python Developer

Front-End Software Developer – Mid/Senior

Senior C++ Software Engineer - Stats, Machine Learning

Senior Data Engineer

Cloud Data Engineer

Senior Data Engineer - Python / SQL / AWS - Fully Remote

This job is with Skyscanner, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.Focused. Encouraging. Honest. We need your expertise to help us do something great for our travellers: make booking stays and journeys more sustainable and straightforward. This involves technical challenges and the latest technology, from machine learning and cloud services to world-class APIs!At Skyscanner, we're revolutionizing the travel experience with cutting-edge AI and data-driven solutions. As a Senior Engineer in the Machine Learning Operations squad, you'll play a critical role in developing the infrastructure that empowers our data science teams to improve and deploy the models that are making an ever-greater contribution to our product experience. You'll typically be working in Java or Python, and with a technology stack that includes AWS, Kinesis, S3, Kubernetes, Spark, Airflow, gRPC, New Relic, Databricks, and more.This role requires expertise in distributed systems, microservices, and data pipelines, combined with a strong focus on observability and the ability to leverage vendor technologies to deliver impactful solutions. While this is not an ML development role, familiarity with the machine learning lifecycle is an advantage. Your ability to solve problems collaboratively with your teammates, and your passion to learn. You'll be able to break down problems into bite-size chunks and deliver them with high quality.Key ResponsibilitiesDistributed Systems Development : Design and build scalable distributed systems using Java-based microservices and Python batch processing to support our ML models, evaluation, and observability.Model Lifecycle : Create and maintain robust model deployment pipelines using PySpark and Databricks, ensuring efficient and reliable data flow across AI systems.Vendor Integration : Identify and leverage vendor capabilities (e.g., AWS, Databricks, and other cloud services) to deliver high-quality solutions that align with organizational goals.Observability Solutions : Develop monitoring and observability systems to track model performance, detect anomalies, and ensure outputs align with business and ethical standards.Collaboration with Specialists : Work closely with cross-functional teams, including Security, Data Science and Product, to ensure comprehensive and secure solutions.Knowledge Sharing : Act as a mentor and technical leader within the squad, fostering collaboration and growth among team members.Complementary skills for this roleTechnical Expertise : Extensive experience with distributed systems engineering, including designing and implementing Java-based microservices and Python batch jobs.Observability Knowledge : Deep understanding of observability principles, including monitoring, logging, and real-time system insightsData Engineering Skills : Proficiency in building data pipelines using PySpark and Databricks, with a strong understanding of data flow and processing.Cloud Vendor Experience : Hands-on experience leveraging vendor technologies like AWS and Databricks to deliver scalable, robust solutions..AI/ML Lifecycle Awareness : Familiarity with the machine learning lifecycle (e.g., tools like MLflow, Data Bricks Model Serving) and its integration into production systems.Collaboration : Strong interpersonal skills with the ability to work effectively across teams, including specialists in security and data science.Problem-Solving Skills : A proactive and innovative approach to tackling complex technical challenges.Skyscanner is a hybrid working company and most roles can be either Full Time or Part Time. We believe when people meet regularly in person, we are better able to innovate, learn, collaborate and inspire. We ask people to be in the office on average 8 days per month.Already a global leader in travel, we want to elevate the way we work to a whole other level. In return, you'll get meaningful things like medical insurance, headspace subscriptions, a home office allowance, and the option to buy more holiday. You'll have the opportunity to work from any country for 4 weeks a year, and 30 days in our other global offices. Everything, in other words, to help you relax and give your best.For more details on Engineering at Skyscanner, check our Engineering Blog and follow Skyscanner Engineering on Twitter.#LI-DNI

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.

Veterans in Machine Learning: A Military‑to‑Civilian Pathway into AI Careers

Introduction Artificial intelligence is no longer relegated to sci‑fi films—it underpins battlefield decision‑support, fraud detection, and even supermarket logistics. The UK Government’s 2025 AI Sector Deal forecasts an additional £200 billion in GDP by 2030, with machine‑learning (ML) engineers cited as the nation’s second most in‑demand tech role (Tech Nation 2024). The Ministry of Defence’s Defence AI Strategy echoes that urgency, earmarking £1.6 billion for FY 2025–28 to embed ML into planning, logistics, and autonomous systems. If you have ever tuned a radar filter, plotted artillery trajectories, or sifted sensor data for actionable intel, you have already worked with statistical modelling—the backbone of machine learning. This guide shows UK veterans how to reframe military experience for ML roles, leverage MoD transition funding, and land high‑impact positions building the models shaping tomorrow’s defence and commercial landscapes. Quick Win: Bookmark our live board for Machine‑Learning Engineer roles to see who’s hiring today.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.