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)
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Work end-to-end:
problem definition modeling evaluation- production integration
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Collaborate with engineers to integrate models into scalable, reliable systems
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Design experiments and define metrics to measure performance and impact
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Continuously improve models based on data, feedback, and real-world usage
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Contribute to improving the reliability and robustness of AI systems, including LLM-based applications where relevant
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Apply model adaptation techniques where appropriate, such as fine-tuning, parameter-efficient tuning, or feedback-driven optimization
- Basic Qualifications -
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Ph.D., MS, or Bachelor's degree in a quantitative field (CS, Statistics, Engineering, etc.)
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2+ years (PhD/MS) or 4+ years (BS) of industry experience in Applied ML / AI
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Strong foundation in machine learning and statistics
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Experience building and deploying ML systems in production
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Proficiency in Python and working with large datasets
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Experience with experimentation, evaluation, and data analysis
- Preferred Qualifications -
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Experience with LLMs, including:
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RAG systems, prompt design, and evaluation
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Experience with model adaptation techniques, such as:
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fine-tuning, parameter-efficient tuning (e.g., LoRA, adapters)
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Familiarity with reinforcement learning or feedback-driven optimization approaches
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Experience working with large-scale data systems (e.g., Spark, Hive, Presto)
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Familiarity with reliability or safety considerations in AI systems
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Experience in domains such as security, fraud, or risk
