Senior Software Engineer - Distributed Systems (MLOps)

myGwork
Edinburgh
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Software & Data Engineer (Java/Python)

Senior AI Software Engineer - Gen AI & NLP

Senior Data Engineer - Advertising Tech

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

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

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.