Remote Dev Jobs
2 jobs available
Clera · remoteRemote57y ago
About the Role Join a fast-growing, mission-driven climate tech company using AI and advanced satellite imagery to help electric utilities manage vegetation risks — preventing wildfires, reducing outages, and building a more resilient energy grid. You'll be part of a multidisciplinary Vegetation Modeling team working at the intersection of geospatial data, machine learning, and real-world environmental impact. As a Senior Geospatial Machine Learning Engineer , you'll spend most of your time working within small, focused groups to experiment with, build, and improve algorithms that help understand how vegetation affects utility infrastructure. Past work has included co-registering imagery, locating critical energy infrastructure, and identifying tree species, heights, and health. Current work spans maintaining and improving these solutions as well as developing new features to assess wildfire risk and evaluate vegetation management strategies. What You'll Do Develop new vegetation intelligence products using standard geospatial Python libraries alongside machine learning and deep learning tools. Support existing products through data exploration, model improvements, and bug fixes — working regularly with QGIS , Dagster , Sentry , and Grafana . Lead projects and initiatives end-to-end: own planning, execution, and delivery, ensuring the value of contributions is clearly communicated to stakeholders across the organisation. Build tooling and processes to measure the performance and business value of your team's work, supporting data-driven prioritisation decisions. Collaborate closely with upstream data ingestion teams and downstream delivery/refinement teams throughout the full scientific product lifecycle. Contribute to shaping team culture, processes, and technical direction at an early-stage, high-growth company. What We're Looking For Required 8–10+ years of relevant experience in machine learning engineering, geospatial engineering, or a closely related field. St
Prima · 🇬🇧 London, United KingdomRemote57y ago
Are you looking for a new challenge? Fancy helping us shape the future of motor insurance? Prima could be the place for you. Since 2015, we’ve been using our love of data and tech to rethink motor insurance and bring drivers a great experience at a great price. Our story began in Italy, where we’ve quickly become the number one online motor insurance provider. In fact, we’re trusted by over 5 million drivers. And now we’re expanding to help millions more drivers in the UK and Spain. To help fuel that growth, we need a Software Engineer- Machine Learning to join our Engineering team. This team is the beating heart of Prima. You’ll be joining over +350 engineers across software development, infrastructure, operations and security. Fueled by curiosity, experimentation and collaboration, you’ll help deliver scalable, impactful solutions that shape the future of insurance. Excited to make an impact? Here are the details The team mission is to exploit the power of machine learning to revolutionize the insurance claims process. To help fuel that growth, we need a Software Engineer to join our Claims Machine Learning team. What you’ll do Leverage Machine Learning and advanced data analytics to identify risks and opportunities in our claims management platform. Analyze and interpret complex data to support the claims management lifecycle. Contribute directly to the company’s growth and long-term success through actionable insights. Collaborate with cross-functional teams to transform data into intelligence and intelligence into action What we’re looking for Software Engineer First: You’re a strong software engineer at heart: you write clean, maintainable, and production-ready code. While you're comfortable handling data, generating insights, and training machine learning models, you approach these tasks with a software engineering mindset, prioritizing scalability, reliability, and performance. Analytical Prowess: Strong quantitative, logical, and analytical skills are essen