AI Engineer

• Build production services using Python, FastAPI and Pydantic models.
• Develop APIs and backend workflows that support AI-enabled document and contract intelligence.
• Work with PostgreSQL, SQLAlchemy and Alembic to model, migrate and operate reliable data layers.
• Build and maintain asynchronous job systems, including queue processing, retries, idempotency, dead-letter handling, cancellation, progress tracking and failure recovery.
• Use Redis for caching, transient state, job status and coordination, with sensible reliability patterns such as timeouts, circuit breakers and graceful degradation.
• Build React and TypeScript interfaces that consume backend services cleanly and provide users with transparent job states, useful error handling and polished interaction patterns.
• Use TanStack Query for server state, including query key factories, cache invalidation, optimistic updates and polling patterns for long-running AI jobs.
• Support document-heavy workflows, including ingestion, retrieval, structured extraction, redlining, rich text editing and AI-assisted review.
• Build features that respect enterprise security expectations, including authentication, authorisation, secure data handling and auditability.
• Operate within Azure-based environments, including services such as Blob Storage, Key Vault, managed databases, Redis and containerised deployments.
• Instrument production systems using Application Insights or equivalent observability tooling.
• Use AI coding tools to accelerate implementation, debugging, refactoring, testing and documentation, while maintaining high code quality.
• Improve developer experience by reducing boilerplate, improving test coverage, simplifying local setup and documenting repeatable patterns.
• Move fluidly between infrastructure and product delivery when needed.

Full Stack AI Engineer

Computer Vision Engineer

• Developing and improving football ball and player tracking algorithms using multi-camera systems
• Contributing to multi-view geometry solutions for accurate tracking across the field of play
• Supporting approaches to handling occlusion and recovery in fast-paced sports scenarios
• Enhancing existing, mature computer vision systems used in live production
• Optimising algorithms with performance and reliability in mind, including CPU and GPU considerations
• Working in an agile environment to deliver robust, production-ready solutions

Computer Vision Engineer – Algorithms

Machine Learning Engineer

• design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels
• plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability
• contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions
• partner with product managers, data scientists, and engineers to define what success looks like and how to measure it
• own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration
• integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks
• help shape the future of AI-assisted development within the team, exploring how tools can accelerate experimentation and delivery

Senior Machine Learning Engineer – Messaging Platform

• You'll build and own the AI systems that make Live Breach feel like a real incident rather than a scripted exercise.
• The AI attacker agent - an autonomous LLM-powered agent that receives a threat actor profile, a network briefing, and a configured attack chain, then executes it against a live environment.
• The exercise orchestration layer - a parallel system that monitors the network during a live exercise, recognises which attack techniques have executed, listens for correct containment and eradication actions from participants, and surfaces investigation tasks tied to real attacker behaviour.
• Prompt engineering for both red and blue team agent components, in close collaboration with our content engineering team
• Integration with adversary emulation tooling for realistic technique execution
• User emulation and noise generation — simulating realistic background activity so participants must distinguish real attacker behaviour from normal log volume
• Documentation and architecture that allows the broader engineering team to operate and debug the AI layer without dependency on any single person.

• Design, build, and ship production-grade machine learning systems that power content safety and policy enforcement at Spotify scale
• Own and lead key technical initiatives across detection, classification, and policy evaluation systems
• Develop and maintain ML models for content moderation, including multimodal and LLM-based systems
• Build robust evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops
• Drive experimentation to improve model performance, reliability, and fairness in safety-critical systems
• Collaborate closely with cross-functional partners in Trust & Safety, Legal, and Public Affairs to align on policy and enforcement needs
• Provide technical leadership within the team, mentoring engineers and contributing to ML strategy and prioritization
• Represent technical decisions and trade-offs in stakeholder discussions and influence product direction

Senior Machine Learning Engineer – Policy & Safety

• Design, build and maintain end-to-end automation workflows
• Develop robust integrations between SaaS systems
• Build internal tools, scripts and lightweight services
• Identify and implement opportunities to embed AI and LLM capabilities into workflows
• Own the end-to-end reliability of solutions by implementing logging, monitoring, alerting and secure configuration practices

Automation and AI Engineer

• Leading the design and evolution of ball and player tracking algorithms using multi-camera systems
• Owning multi-view geometry solutions across entire tracking pipelines
• Driving improvements in occlusion handling, recovery and system robustness in live sports environments
• Overseeing mature computer vision systems used in real-time production
• Guiding performance optimisation across CPU and GPU workloads
• Ensuring consistency and reliability across cross-platform deployments
• Supporting integration into cloud-enabled and distributed systems
• Influencing how systems are designed, built and scaled across the organisation
• Lead the development of computer vision systems from concept through to production at a system level
• Set technical direction and standards across algorithmic and engineering approaches
• Own critical components and ensure long-term maintainability, scalability and quality
• Work closely with discipline leads and engineers across CV, tracking, tooling and platform teams
• Drive architectural discussions around performance, scalability and deployment
• Identify limitations in existing systems and lead pragmatic improvements
• Mentor and support engineers through technical guidance, reviews and collaboration
• Act as a technical escalation point for complex challenges

Principal Computer Vision Engineer

• Drive technical excellence by leading design and implementation of software solutions
• Provide mentorship to engineering teams and foster cross-functional collaboration
• Contribute to strategic planning for product development
• Accountable for AI platform architecture and AI risk posture
• Ensure production-readiness of AI systems

Principal AI Engineer

AI Research Scientist

• Design and develop agentic systems that exhibit autonomous decision-making, goal-setting and task completion
• Design and optimize agentic memory and memory agent frameworks, and multi-agent-interactions
• Finetune and deploy various LLMs and adaptors for on-device and on-server applications using various optimization techniques including reinforcement learning and supervised finetuning
• Contribute code libraries by adding new features and improving its capabilities
• Lead the development of software prototypes and experimental systems, ensuring high code quality and maintainability
• Collaborate with a multidisciplinary team of researchers and engineers to integrate research findings into Samsung products and services.

AI Research Intern

• Architect and maintain knowledge graphs (KG), leveraging ontologies and graph databases.
• Design and implement RAG and GraphRAG pipelines to enhances reasoning for structured, contextual and dynamic data.
• Evaluate and optimize KG, RAG and GraphRAG systems for accuracy, latency and memory trade-off.
• Finetune and deploy various LLMs and adaptors for on-device applications.
• Build robust data generation and collection pipelines for model training and KG, RAG or GraphRAG construction.
• Contribute code libraries by adding new features and improving its capabilities.
• Evaluate the performance of algorithms and systems through rigorous experimentation and statistical analysis.
• Lead the development of software prototypes and experimental systems, ensuring high code quality and maintainability.
• Collaborate with a multidisciplinary team of researchers and engineers to integrate research findings into Samsung products and services.

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