Responsibilities Research, Develop, and implement machine learning based tools across LLMs, Multimodal systems, Generative AI systems Develop training pipelines, architecture, and prototyping for ML algorithms Work on productising research prototypes Conduct experiments to benchmark new techniques and evaluate model behavior Develop systematic evaluation methodology: test sets, accuracy metrics, citation quality scoring, false positive/negative analysis Deploy AI tools to engineering teams with structured pilots, baseline measurement, and documented adoption outcomes
Requirements Required Qualifications 2+ years in developing ML systems with focus on LLM-based products Proven understanding of fundamental data structures and the ability to apply them to solve complex problems. Experience with Pytorch, Tensorflow GenAI engineering including concepts relating to scaffolding, harnessing, and RAG pipelines Development experience with retrieval pipeline skills (vector databases, chunking strategies, re-ranking, citation grounding) Experience with Containerization (Docker/Kubernetes)
Preferred
Qualifications Understanding and deployment of Reinforcement Learning based tools Understanding of mathematics, particularly linear algebra and probability theory Experience with distributed processing frameworks and ElasticSearch Prior knowledge of multimodal systems and VLM systems Experimentation and deployment of Local GenAI model deployment using tools like Olama Experience building multi-step agentic workflows
Benefits
What
We Offer The opportunity to work on a groundbreaking product that will redefine urban transportation. A dynamic, fast-paced, and collaborative work environment with a brilliant and passionate team. Competitive salary and benefits. A culture of innovation where your ideas can directly impact the future of flight

