We are looking for a Fullstack Software Engineer to join our team and help build robust,
scalable systems that bring our state-of-the-art generative AI systems to the wider audience.
This fullstack role has a strong backend emphasis, requiring deep experience in server-side
architecture, API design, distributed systems, and frontend frameworks, along with a solid
engineering mindset and the ability to work cross-functionally with ML engineers, product,
research, and creative teams. As a member of our fullstack engineering team, you’ll shape
core infrastructure, set best practices, communicate with stakeholders and mentor your
peers.
Key Responsibilities
Design and implement scalable backend architectures for AI-powered creative tools
and workflows.
Build high-performance APIs and microservices that handle generative AI
inference/training and video/audio processing.
Design and implement user interfaces that make complex generative AI capabilities intuitive and accessible.
Develop robust data pipelines for ingesting, processing, and storing large-scale media content.
Design and maintain database schemas, caching layers, and data storage solutions for multimedia content.
Build authentication, authorisation, and security systems for enterprise-grade applications.
Optimise backend performance for low-latency AI inference and high-throughput media processing.
Integrate backend systems with ML training and inference pipelines.
Implement monitoring, logging, and alerting systems for production backend services.
Collaborate with ML engineers to optimise model serving and deployment infrastructure.
Must Haves
5+ years of experience in fullstack software development.
Strong expertise in server-side programming languages (we use Python).
Proven experience designing and implementing RESTful APIs and microservices
architectures.
Proficiency in modern JavaScript/TypeScript and at least one major frontend
framework (React, Vue.js, or Angular).
Deep understanding of database design, optimisation, and management
(PostgreSQL, Neo4j, Redis).
Experience with cloud platforms (AWS, GCP, Azure) and infrastructure as code
(Terraform).
Proficiency with containerisation and orchestration technologies (Docker,
Kubernetes).
Strong knowledge of distributed systems, message queues, and event-driven
architectures.
Experience with high-performance computing, parallel processing, and optimisation
techniques.
Familiarity with CI/CD pipelines, automated testing, and DevOps practices.
Understanding of system design principles, scalability, and performance optimisation.
Experience with caching strategies, load balancing, and CDN implementation.
Experience with CSS frameworks and responsive design.
Strong problem-solving skills and attention to detail.
Nice to Have
Experience with ML and/or computer vision frameworks like PyTorch, Numpy or
OpenCV.
Knowledge of ML model serving infrastructure (TensorFlow Serving, TorchServe,
MLflow).
Knowledge of WebGL, Canvas API, or other graphics programming technologies.
Familiarity with big data technologies (Kafka, Spark, Hadoop) and data engineering
practices.
Background in computer graphics, media processing, or VFX pipeline development.
Experience with performance profiling, system monitoring, and observability tools.
Understanding of network protocols, security best practices, and compliance
requirements.
Open-source contributions or technical writing experience.
Entrepreneurial mindset or experience working with startups or fast-paced teams.