About this role
About Centific
Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.
Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.
About Job
We are looking for entry level Data Scientists/Applied Scientists (through student worker program) with strong Python coding skills, passion for AI quality, the ability to learn, and hands-on experience in delivering exceptional AI experiences, which our customers have come to expect.
Responsibilities
- Constructing and validating datasets for long-horizon coding tasks. Responsibilities include collecting real-world software engineering tasks, constructing sequences of related tasks, and validating datasets for training and evaluating coding agents. This dataset is essential for PhD dissertation research on long-horizon agentic reinforcement learning. The construction and validation process requires extensive CPU resources and large-scale model API access that are not available at the necessary scale through university resources.
- Developing, training, and comparing agentic reinforcement learning algorithms.Daily work involves implementing reinforcement learning algorithms, training coding agents, and conducting comparative experiments to study how agents learn from decisions across multiple tasks. These experiments are a central component of my dissertation research on long-horizon agentic reinforcement learning and credit assignment. Training and comparing multiple algorithms and models requires substantial GPU resources provided by Microsoft's research infrastructure.
- Evaluating the generalization of coding agents on independent benchmarks. Responsibilities include evaluating trained coding agents on additional long-horizon software engineering benchmarks, analyzing their performance, and comparing different reinforcement learning methods. These evaluations are necessary for my dissertation to establish whether the proposed algorithms generalize beyond their training data and perform reliably on previously unseen tasks. The experiments require extensive GPU computation and model API usage supported by Microsoft's research infrastructure.
Required/Minimum Qualifications:
- Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
- 3 + years’ experience coding in Python and experience in natural language processing or deep learning.
- 1+ years' experience in a deep learning library, such as PyTorch or TensorFlow.
Payrate : $40/hr
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