Staff AI Engineer

Description:

abra R&D is looking for a Staff AI Engineer!

abra R&D is looking for a Staff AI Engineer to help build a next-generation agentic analytics platform, the first real-time database optimized for AI agents at scale.

We are looking for a Staff AI Engineer to lead the design of data-driven models and analytical systems powering agentic analytics. This role focuses on causal inference, time-series modeling, and large-scale data analysis, working on real-time, high-dimensional datasets.

What you’ll do:

• Design and implement causal inference models to understand and drive decision-making
• Develop time-series models for real-time analytics and forecasting
• Build machine learning models on large-scale, structured and unstructured data
• Define statistical methodologies for agentic analytics and decision systems
• Work closely with AI and engineering teams to integrate models into production systems
• Analyze complex datasets and extract actionable insights
• Design experiments, evaluation frameworks, and data-driven feedback loops
• Contribute to the overall data science strategy and architecture

• 7–10+ years of experience in data science / applied ML / quantitative research
• PhD or M.Sc in Computer Science, Statistics, Mathematics, or related field
• Strong programming skills in Python

Causal Inference (required experience with several of the following)

• Propensity Score Matching (PSM)
• Inverse Probability Weighting (IPW)
• Difference-in-Differences (DiD)
• Instrumental Variables (IV)
• Regression Discontinuity Design (RDD)
• Causal Forests / Double Machine Learning (DML)

Time Series (required experience with several of the following)

• ARIMA / SARIMA
• State Space Models / Kalman Filters
• Prophet
• VAR (Vector Autoregression)
• LSTM / Temporal Deep Learning models
• Transformer-based time-series models
• Anomaly detection in time-series (e.g., STL decomposition, change point detection)

Machine Learning & Data Stack

• Strong experience with scikit-learn, XGBoost, LightGBM
• Experience with deep learning frameworks (PyTorch or TensorFlow)
• Experience with data processing (Pandas, NumPy, Spark)
• Experience working with large-scale or real-time data systems

Strong Plus

• Experience with causal inference in production systems
• Background in analytics platforms or data products
• Experience working with time-series + event-driven data
• Familiarity with LLM-based systems or agentic analytics
• Experience designing experimentation platforms (A/B testing, uplift modeling)