About The Role
We are looking for a
Senior Applied Scientist to build and deploy machine learning and AI solutions in production. You will work on high-impact problems, taking ideas from concept to real-world systems, and help improve the reliability and effectiveness of AI-driven applications.
- What the Candidate Will Do ----
- Build and deploy ML/AI models in production across a range of problem areas (e.g., classification, prediction, anomaly detection, risk scoring)
- Work end-to-end:
- problem definition → modeling → evaluation → production integration
- Collaborate with engineers to integrate models into scalable, reliable systems
- Design experiments and define metrics to measure performance and impact
- Continuously improve models based on data, feedback, and real-world usage
- Contribute to improving the reliability and robustness of AI systems, including LLM-based applications where relevant
- Apply model adaptation techniques where appropriate, such as fine-tuning, parameter-efficient tuning, or feedback-driven optimization
- Basic Qualifications ----
- Ph.D., MS, or Bachelor's degree in a quantitative field (CS, Statistics, Engineering, etc.)
- 2+ years (PhD/MS) or 4+ years (BS) of industry experience in Applied ML / AI
- Strong foundation in machine learning and statistics
- Experience building and deploying ML systems in production
- Proficiency in Python and working with large datasets
- Experience with experimentation, evaluation, and data analysis
- Preferred Qualifications ----
- Experience with LLMs, including:
- RAG systems, prompt design, and evaluation
- Experience with model adaptation techniques, such as:
- fine-tuning, parameter-efficient tuning (e.g., LoRA, adapters)
- Familiarity with reinforcement learning or feedback-driven optimization approaches
- Experience working with large-scale data systems (e.g., Spark, Hive, Presto)
- Familiarity with reliability or safety considerations in AI systems
- Experience in domains such as security, fraud, or risk
