Applied Scientist

Responsibilities:

• Design, develop, evaluate, and productionize machine learning and AI solutions for Kily's products.
• Create advanced AI/ML models using Python and modern frameworks to enhance intelligent systems.
• Spearhead generative AI initiatives involving LLM experimentation, evaluation, and application development.
• Build AI-powered features and workflows that drive product capabilities forward.
• Design rigorous experiments to measure model quality, reliability, performance, and business impact.
• Leverage complex datasets for robust feature engineering, training pipelines, and evaluation methodologies.
• Solve real-world problems using NLP, deep learning, statistics, and machine learning techniques.
• Collaborate with Engineering, Product, and Design teams to translate requirements into effective AI solutions.
• Drive initiatives from research prototyping through production deployment and continuous optimization.
• Detect model failures, resolve bottlenecks, and improve accuracy, latency, reliability, and cost efficiency.
• Monitor emerging AI trends and evaluate their applicability to product needs.
• Document experiments and technical decisions clearly for cross-functional teams.

Requirements:

• Strong background as an applied scientist or equivalent in machine learning/AI roles.
• Expertise in Python programming.
• Solid grasp of ML fundamentals, statistical methods, model development, and evaluation.
• Versatility in deep learning and modern AI/ML techniques.
• Deep understanding of NLP and Transformer-based architectures.
• Proven experience building, training, fine-tuning, evaluating, and deploying models.
• Sophisticated knowledge of generative AI and large language models (LLMs).
• Focus on designing production-ready AI systems rather than just research prototypes.
• Mastery of experimentation, offline/online evaluation, model performance metrics, and reliability.
• Experience with APIs, data pipelines, model-serving systems, and production workflows.
• Exceptional problem-solving and analytical skills.
• Excellent communication abilities to work effectively with diverse teams.
• Bias for experimentation: Testing hypotheses quickly while maintaining scientific rigor.
• Collaboration-ready mindset to turn AI capabilities into tangible products.

AI-First Mindset:

• We seek scientists who actively apply modern AI techniques to real-world engineering problems.
• Highly valuable experience includes: LLMs/Generative AI; Transformer architectures and fine-tuning; OpenAI, Anthropic, Gemini APIs; RAG and vector databases; Agentic workflows; Prompt engineering; LangChain/LangGraph frameworks; Model evaluation and cost optimization.
• We value the ability to translate research into practical, deployable production capabilities.

Good to Have:

• Experience building AI-native or AI-powered SaaS products.
• Work with LLM fine-tuning, alignment, inference optimization.
• Familiarity with PyTorch, TensorFlow, scikit-learn.
• Cloud infrastructure experience (AWS/GCP/Azure).
• MLOps practices and model serving expertise.
• Data systems knowledge: PostgreSQL, Redis, MongoDB, vector databases.
• Docker and Kubernetes proficiency.
• Experience with distributed systems at scale.
• Published research or strong technical portfolio.