Senior Machine Learning Engineer

The candidate will have responsibilities across the following functions:

Technical and Architectural Leadership:

• Define and own the ML and advanced analytics architecture supporting HR, benefits, and payroll products.
• Design end-to-end, production-grade ML systems from data ingestion and feature engineering to model serving, monitoring, and retraining.
• Lead the selection and optimization of algorithms (tree-based models, deep learning, time-series forecasting, and GenAI) with tradeoffs across accuracy, latency, scalability, and cost.
• Establish best practices for model governance, explainability, bias detection, and compliance.

Advanced Modelling and Forecasting:

• Drive the development and validation of time-series and forecasting models (ARIMA/SARIMA, Prophet, state-space models, LSTM/transformers) for workforce planning, attrition, and financial forecasting.
• Champion advanced experimentation, model evaluation frameworks, and statistical rigor across teams.
• Leverage GenAI/LLMs where appropriate to enhance product intelligence and user experience.

MLOps and Production Excellence:

• Partner with DevOps and Platform teams to build robust MLOps pipelines using CI/CD, automated retraining, monitoring, and alerting.
• Ensure reliable, secure, and scalable model deployment using containerized microservices.
• Define SLAs, performance benchmarks, and operational metrics for ML services in production.

Leadership and Collaboration:

• Lead, mentor, and grow a team of senior and mid-level data scientists, fostering a culture of technical excellence and ownership.
• Work closely with Product, Engineering, Security, and Compliance to translate business needs into scalable ML solutions.
• Act as a trusted advisor to stakeholders, influencing product strategy and the long-term data science roadmap.

Requirements:

• 6+ years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production.
• Familiarity with the setup and use of various open-source LLM foundation models.
• Experience with creating and using vectorized databases for data storage and retrieval.
• Familiarity with LLM architecture patterns such as RAG and FLARE.
• Hands-on experience with LLM pretraining, LLM fine-tuning, RLHF, distillation, parameter-efficient methods like LoRA, and quantization.
• Experience with observability, monitoring, debugging, and performance evaluation for LLM training/inference systems.
• A bachelor's degree in computer science, engineering, mathematics, or a related field is required.