Product and Tech • Flexible remote • UK - LONDON Weare looking for a Machine Learning Engineer with strong datascience roots and real-world deployment experience to help shape,optimise and scale our ML stack as we continue to grow. YuLife isan award-winning InsurTech company and an employee benefitprovider. We’re the world’s first “lifestyle insurance” company.While other insurers are there for people at the point of death orillness, we engage with people every day to help them live betterlives. To do that, we’ve built an award-winning app that rewardspeople for building healthy habits. It includes the best wellbeingand digital health tools in the world. Customers can earn vouchersfrom great brands like Amazon, Tesco or ASOS for doing simplethings like walking or practicing mindfulness. They can even dogood by planting trees or cleaning the oceans from the app. Ourclients include Tesco, Capital One and Fujitsu, and we’ve beenranked the #1 employee benefit in the UK on Trustpilot. Morerecently, YuLife was recognised by CX Insurance Awards as ‘BestInsurtech 2024’ and the 8th Fastest Growing Technology Company inthe UK in the prestigious 2023 Deloitte Technology Fast 50. Therole: We’re looking for an experienced Machine Learning Engineer tojoin our growing data science team. This is a hands-on role forsomeone who has grown from a data science background and hastransitioned into machine learning engineering. You'll be workingalongside our Lead Data Scientist and the wider data team to evolveand scale our ML infrastructure. You’ll play a key role inbuilding, deploying and optimising both real-time and batch MLmodels, ensuring they run smoothly and add measurable value to ourproduct and users. Day to day responsibilities include, but are notlimited to: 1. Designing, building, and deploying production-gradeML pipelines and services in Python 2. Collaborating with datascientists and data engineers to productionise models and guidethem through deployment best practices 3. Improving thescalability, reliability, and automation of our ML infrastructureand workflows 4. Developing systems for both real-time(low-latency) and batch inference use cases 5. Working with AWS andother cloud services (e.g. Fargate, ECS, Lambda, S3, SageMaker,Step Functions) to deploy and monitor models in production 6.Implementing and maintaining CI/CD pipelines for ML workflows,ensuring repeatability and robust version control 7. Setting upmonitoring and alerting frameworks to track model drift, dataquality, and inference health 8. Leveraging orchestration toolssuch as Dagster, Airflow, or Prefect to manage and scale MLworkflows 9. Supporting ongoing infrastructure migration oroptimisation initiatives (e.g. improving cost efficiency, latency,or reliability) 10. Partnering with product and engineering teamsto ensure ML solutions are aligned with business goals, and thatperformance metrics and outcomes are clearly tracked 11.Documenting and continuously evolving ML engineering best practicesThe ideal candidate will have: 1. 3+ years of hands-on experiencein ML engineering, ideally having started in data science with astrong foundation in a quantitative field such as mathematics,statistics, physics, or computer science 2. Strong Pythonprogramming skills with experience in building and maintainingproduction ML applications 3. Demonstrated experience deploying MLmodels into production environments, including both batch andreal-time/streaming contexts 4. Proficiency working withdistributed computing frameworks such as Apache Spark, Dask, orsimilar 5. Experience with cloud-native ML deployment, particularlyon AWS, using services like ECS, EKS, Fargate, Lambda, S3, and more6. Familiarity with orchestration and workflow scheduling toolssuch as Dagster, Airflow, or Prefect 7. Knowledge of CI/CD bestpractices and tools (e.g. GitHub Actions, Jenkins, CodePipeline) 8.Exposure to monitoring and observability tools for ML systems (e.g.Prometheus, Grafana, DataDog, WhyLabs, Evidently, etc.) 9.Experience in building parallelised or distributed model inferencepipelines Nice-to-Have Skills 1. Familiarity with feature storesand model registries (e.g. Feast, MLflow, SageMaker Model Registry)2. Knowledge of model versioning, A/B testing, and shadowdeployments 3. Experience implementing or contributing to MLOpsframeworks and scalable deployment patterns 4. Experience withcontainerisation and container orchestration (Docker, Kubernetes)5. Comfortable working in a fast-paced, cross-functional team withproduct and engineering stakeholders What you’ll get: We like togive more than we take so here are some of our benefits: -Potential to earn share options - 6x salary life assurance - HealthInsurance - Income protection - 3% company contribution to pensionvia salary sacrifice scheme - 25 days Annual Leave + 1 Love beingYu (e.g your birthday, moving house anything else that is for Yu!)- Access to the YuLife app (which includes a tonne of well-beingrewards, discounts and exclusive offers as well as access toMeditopia and Fiit ) - £20 per month to a "be your best Yu" budget- Unlimited Monthly professional coaching with More Happi - Remoteand flexible working - Currently our lovely office in Shoreditch isavailable if people want (and only if they want) to use it Here atYuLife our values encompass Love Being Yu and as a result we’recommitted to diversity and inclusion. We are an Equal OpportunityEmployer and do not discriminate against any employee or applicantfor employment because of race, colour, sex, age, national origin,religion, sexual orientation, gender identity and/or expression,disability or any other protected class.#J-18808-Ljbffr
