Junior Software Engineer

You’ll join the AI Engineering team to build and deliver the innovative, high-performance model and workflow to Possibl.ai clients. Your mission is to ensure our models, AI-driven workflow and solutions are not just powerful but reliable and intuitive for every user.

Your work will help in building, integrating and delivering AI solutions spanning text, vision and data while ensuring these tools directly solve specific customer business challenges. You’ll be the person turning "impossible" research into everyday creative superpowers for our community. This role offers deep exposure to ML, Deep Learning, generative AI, complex workflows, including diffusion and transformer models, while developing your confidence in performance optimisation, GPU environments and reproducible workflows. You’ll work alongside senior MLOps engineers and research scientists, gaining context and stepping into more ownership as you grow.

If you’re excited about supporting our client in harnessing AI to drive efficiency, innovation and want to sharpen both your modelling and engineering depth, you’ll thrive here.

What You’ll Do Model Development and Evaluation

• Collaborate with research scientists to adapt and fine-tune diffusion and transformer-based models.
• Run experiments and evaluate model outputs to improve quality and consistency.
• Contribute to performance improvements such as batching, caching or quantisation with guidance.
• Help identify bottlenecks and opportunities for generation improvements. ML Engineering and Systems Development
• Build and maintain training and inference pipelines.
• Use GCP, Azure and Kubernetes to experiment and deliver models and workflows.
• Use Docker and internal tools to standardise training, experimentation and deployment.
• Contribute to shared APIs and model-serving components. MLOps and Reliability
• Support dataset handling, experiment tracking and model versioning.
• Extend monitoring dashboards and alerts to maintain system performance.
• Maintain reproducibility across checkpoints, datasets and experiments. Cross-Team Collaboration
• Work with product teams to support integration of generative features.
• Share learnings and contribute to a supportive engineering culture. Skills We Value
• Strong Python engineering skills and familiarity with ML workflows.
• Experience with PyTorch or TensorFlow in project, research or production settings.
• Understanding of model training or fine-tuning.
• Interest in improving inference performance and efficiency over time.
• Clear, thoughtful communication and collaborative working style.
• Comfortable with modern automation tools and curious to learn unfamiliar technologies. Nice to Have
• Exposure to diffusion, transformer or audio and video analysis models.
• Familiarity with Azure, Docker, Kubernetes or Model orchestration.
• Experience with monitoring or observability tooling.
• Open-source, research or personal ML project contributions. *Background in creative-tech, ML tooling or SaaS environments.