Source Job

• Design, implement, and maintain robust, containerized, and reproducible pipelines for model training, evaluation, and deployment—across both batch and real-time settings.
• Build and manage ML services, APIs, and model serving infrastructure using tools like MLflow, Amazon SageMaker, and Feature Store.
• Set up and maintain monitoring, observability, and alerting systems to ensure high availability and performance (including model/data drift, feature logging, and inference latency).

AUTO1 Group Technology drives innovation in the used car market across Europe. They operate at the intersection of software engineering, data science, and DevOps, helping bring state-of-the-art ML models—such as large-scale recommendation systems and transformer-based neural networks—safely into production.

• Develop scalable MLOps pipelines for model training, validation, deployment, and monitoring using AWS services
• Implement infrastructure as code and CI/CD workflows to support rapid experimentation and reliable production releases
• Collaborate with data scientists to productionize ML models and ensure reproducibility, versioning, and traceability

Experian is a global data and technology company, powering opportunities for people and businesses around the world. A FTSE 100 Index company listed on the London Stock Exchange (EXPN), they have a team of 23,300 people across 32 countries and corporate headquarters are in Dublin, Ireland.

• Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges.
• Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerisation practices.
• Build high-performance data pipelines (ETL/ELT) for both training and real-time inference, ensuring our systems are scalable and reliable.

EDITED is the world’s leading AI-driven retail intelligence platform. They empower the world’s most successful brands and retailers with real-time decision making power. Their environment is dynamic and supportive, encouraging team members to take initiative, innovate, and continuously grow.

• Design, develop, and deploy machine learning models and pipelines using Python
• Build and maintain end-to-end ML systems from data ingestion to model serving
• Write clean, efficient, and maintainable Python code following best practices

Activate Group was named by the Sunday Times as one of the UK’s 100 fastest-growing private companies. They employ more than 700 team members nationwide and work with some of the UK's largest fleets and insurance companies, supporting drivers involved in road incidents.

• Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, quality, and optimisation.
• Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions.
• Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations.

Canva is a design platform that enables users to create professional designs. They have a flagship campus in Sydney, a second campus in Melbourne, and co-working spaces in other locations, with a flexible work environment.

• Design and implement MLOps pipelines to automate model training, deployment, monitoring, and management
• Lead/mentor a team of MLOps Engineers, fostering an inclusive and collaborative environment that encourages innovation and continuous learning
• Collaborate with Data Scientists and ML Engineers to ensure models are production-ready, scalable, and maintainable

Egen is a fast-growing and entrepreneurial company with a data-first mindset. They bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights.

• Own the Pipeline from Cloud to Edge, re-architecting machine learning model deployment to edge devices.
• Build Shadow Mode Infrastructure to test candidate models on production devices silently.
• Drive governance & monitoring by building tooling to monitor model drift and performance from the edge.

Hudl builds great teams and hires the best to foster continuous learning. They provide a supportive culture where employees feel valued, contributing to their recognition as a Top 100 Global Most Loved Workplace by Newsweek.

• Oversee the design, development, and implementation of scalable scoring models to drive business impact.
• Guide and mentor junior data scientists, fostering a culture of innovation and continuous improvement.
• Communicate complex technical concepts to non-technical stakeholders, providing actionable insights and driving data-informed decision-making.

Bondora's mission is to empower people to enjoy life more while alleviating financial stress. Founded in 2008, Bondora has served over 1 million customers and is rapidly growing as they pursue a banking license and expand their investment and loan products across Europe.

• Design, implement, and maintain high-performance ML training and inference platforms.
• Ship tools that allow any ML engineer to deploy a model in minutes, not days.
• Improve scalability, reliability, and cost efficiency of model training and serving systems.

Speechify's mission is to make sure that reading is never a barrier to learning. With nearly 200 people around the globe working in a 100% distributed setting, Speechify's team includes frontend and backend engineers, AI research scientists, and others.

• Build, maintain and improve Torc ML frameworks.
• Use Terraform, AWS Managed Services, EKS, Ray.
• Focus on data ops, ML development pipeline, logging & aggregation.

Torc has been a leader in autonomous driving since 2007. Now a part of the Daimler family, they are focused solely on developing software for automated trucks to transform how the world moves freight. Their culture is collaborative, energetic, and team focused.

• Contribute to our core ML infrastructure.
• Prototype new training frameworks and production-ize solutions at scale.
• Design, optimize and test model integration infrastructure.

Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, LLM's and audio recognition. Clarifai was founded in 2013 and has employees remotely based throughout the United States, Canada, Argentina, India and Estonia.

• Design scalable, future-proof data platforms optimized for AI research workloads.
• Build efficient self-serve data processing pipelines leveraging GCP's advanced services.
• Implement guardrails for cost, quality, and performance.

AssemblyAI is at the forefront of Speech AI, creating powerful models for speech-to-text and speech understanding via an API. They're a remote team of startup veterans and AI researchers looking to build one of the next great AI companies.

• Work with research teams to design and build our training infrastructure
• Prototype new training frameworks and production-ize solutions at scale
• Design, optimize and test model integration infrastructure

Clarifai is a leading AI platform specializing in computer vision, NLP, LLMs, and audio recognition, helping organizations transform unstructured data into structured data. Founded in 2013, they remotely operate across multiple countries with backing from industry leaders, fostering a diverse and equal opportunity workplace.

• Architect, design, and oversee delivery of end-to-end AI/ML solutions.
• Lead cross-functional teams to implement robust ML platforms, pipelines, and applications.
• Communicate the business value and ROI of AI/ML solutions to stakeholders.

Jobgether is using an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. The system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.

• Design, build, and scale enterprise-grade AI/ML systems that power internal workflows and external-facing AI/ML platforms.
• Develop a production-ready Generative AI and MLOps platform with reusable components used to deploy multiple AI solutions across Natera’s business units.
• Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services

Natera is a global leader in cell-free DNA (cfDNA) testing. They are dedicated to oncology, women’s health, and organ health, aiming to make personalized genetic testing and diagnostics part of the standard of care. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions.

• Implement production AI / ML workloads using Ray and Anyscale.
• Advise customers on ML system architecture.
• Partner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows.

Anyscale is on a mission to democratize distributed computing and make it accessible to software developers. They are commercializing Ray, an open-source project creating an ecosystem of libraries for scalable machine learning and are backed by Andreessen Horowitz, NEA, and Addition.

• Train, test, and ship models that power Peec AI’s recommendations.
• Develop algorithms that extract actionable insights from AI search behavior.
• Own the full model lifecycle from experimentation to production deployment.

Peec AI helps customers boost their visibility in AI search. They are a fast-growing Series A startup in Europe.

• Build Enterprise-Scale Infrastructure leveraging infrastructure-as-code to manage complex cloud environments.
• Sustain Platform Health and Performance owning critical systems in production, including reliability and security.
• Enable Teams and Customers to Move Faster creating abstractions and tooling that deploy, run, and scale AI/ML workloads.

Cake is on a mission to make cutting-edge AI accessible to enterprise teams. Backed by top investors, Cake is seeing strong adoption and is positioned for rapid growth in the next 12 months, emphasizing ownership, clear communication, and collaboration.

• Own ML powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics.

Calendly's product enables millions of people to coordinate easily. They are experiencing exciting product growth, making it a great time to consider joining their journey.

• Support and evolve the reliability of platforms used by the AI Research team.
• Ensure production services meet expectations for availability, latency, and operational readiness.
• Build and maintain Kubernetes-based services on GCP using infrastructure-as-code and GitOps.

Algolia is a pioneer and market leader in AI Search, empowering 17,000+ businesses to deliver blazing-fast, predictive search and browse experiences. They have raised $150 million in Series D funding, quadrupling their valuation to $2.25 billion, investing in their market-leading platform.