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This 6-month full-time internship involves designing and building AI agents and automation tools to improve engineering workflows in automotive software, including AUTOSAR and embedded systems. Interns will integrate these solutions with tools like Jira, GitHub, and CI/CD pipelines.
As a Business Analyst for Risk Management, you will bridge the gap between regulatory requirements and software development for insurance reporting solutions. You will work with teams to implement compliance standards like Solvency II and DORA while leveraging AI tools to optimize processes and product development.
Evaluate AI model outputs, create expert-level prompts and datasets, and provide structured feedback to improve model capability in statistics and machine learning.
Senior software engineer evaluates AI-generated code, designs coding challenges, and provides feedback to improve model performance on real-world tasks.
Designs and builds Go-based control-plane services, drivers, and tooling for GPU-accelerated AI/storage integration in Kubernetes, focusing on hybrid/edge/air-gapped deployments with Mirantis K0rdent.
Lead a team of data scientists in São Paulo to deliver AI-driven retail and CPG solutions, translating business challenges into measurable insights using Python, Spark, and advanced analytics.
Build next-gen AI models for retail using transformers and deep learning, turning research into production-ready solutions that power personalisation and forecasting.
Build and scale dunnhumby’s Enterprise AI Platform, designing production-grade AI systems including RAG, agentic workflows, and LLM-powered services using Python, LangChain, and cloud-native tools.
Lead Software Engineer for an AI-native email app, owning architecture across backend, mobile, and desktop while leading a small senior engineering team to turn AI/LLM capabilities into reliable, shippable product experiences.
Monitors and secures AI tools, agents, and models across the company, hunting for unauthorized usage and data risks while supporting compliance with AI governance frameworks like ISO 42001.
Lead the architecture of AMD’s next-gen SoCs, defining features and power/performance trade-offs while collaborating with global design teams to ensure silicon meets market needs.
Staff engineer owning the engineering layer for an AI-native email/app product, designing reliable backend systems, APIs, and client apps that integrate with ML/LLM capabilities while leading a small senior team.
The Senior Staff Engineer (Test Engineering Lead) will lead a team to define and execute test strategies for hardware products, including production test systems, field device debugging, and COTS validation. The role requires extensive experience in hardware and embedded software testing within a fleet safety and AI-powered telematics environment.
Build and maintain Nexxen’s internal AI platform, designing reusable AI capabilities, multi-agent systems, and production-grade AI services using Python, Kubernetes, and modern cloud-native tools.
Lead AI-powered product development from concept to launch, collaborating with engineers and designers to build web, gaming, and automation solutions.
As a Senior Fullstack Engineer, you will architect and develop core features for an AI-driven platform, focusing on RAG systems, microservices, and frontend integration. You will work in an AI-first environment, utilizing coding agents to build scalable, secure, and compliant solutions for enterprise clients.
The Multimedia Designer will create visual and motion content, edit videos, and manage brand assets for an AI SaaS startup. The role involves using design software and AI tools to produce marketing materials while collaborating across teams in a hybrid Amsterdam-based environment.
The Lead Engineer, Machine Learning will build and scale production-ready AI systems, managing the full model lifecycle from training to deployment. The role focuses on creating reliable, high-performance inference systems using Python, PyTorch, and JAX to power proactive, AI-native applications.
Build and maintain scalable data pipelines and ML workflows on GCP, deploying models from notebooks to production using orchestration tools like Airflow.
Embed with customers to design and deploy AI systems that automate repetitive tasks while keeping humans in the loop for judgment, using computer vision and agents.
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