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Senior ML Engineer — Reinforcement Learning and Prediction

Zillwork

Coimbatore · On-site Full-time Senior Today

About the role

About Zillwork

Zillwork is a Singapore-incorporated AI company building technology that creates economic opportunity at scale. We are pre-launch, founder-led, and operating out of Chennai and Coimbatore.

We are technically ambitious and globally minded. Our stack includes fine-tuned multilingual speech models, real-time geo-intelligent matching at scale, and a homegrown AI engine designed from first principles. We are building for markets and communities that existing technology has never seriously attempted to serve.

We are a smart, focused team. Every hire shapes the culture and the product. If you want your work to matter — and you are energised by hard problems over comfort — read on.

The Role

As Senior ML Engineer (RL and Forecasting), you will build two of the platform's most technically complex systems: a PPO reinforcement learning policy for fair multi-objective matching, and a Temporal Fusion Transformer demand forecasting system that predicts platform activity 7 days ahead by geo-cell and category.

What You Will Build

  • PPO policy using RLlib (Ray) — multi-objective reward: quality + fairness + satisfaction
  • Temporal Fusion Transformer for geo-cell demand forecasting at city scale
  • Kafka time-series integration for real-time demand signals
  • ClickHouse aggregations for demand-supply heatmaps
  • Proactive alert system based on predicted demand shifts

What We Are Looking For

  • 5+ years ML engineering with production deployments
  • Reinforcement Learning hands-on: PPO, DQN, or policy gradient in production
  • RLlib (Ray) or Stable Baselines preferred
  • Time-series forecasting: TFT, Prophet, DeepAR, or LSTM in production
  • Apache Kafka for ML feature pipelines
  • Python, PyTorch, Kubeflow

Bonus: Multi-objective RL, geo-cell demand forecasting, ClickHouse real-time analytics

Apply

Subject: Senior ML Engineer RL Forecasting Application

Requirements

  • 5+ years ML engineering with production deployments
  • Hands‑on reinforcement learning experience (PPO, DQN, or policy gradient) in production
  • Experience with RLlib (Ray) or Stable Baselines
  • Time‑series forecasting experience (TFT, Prophet, DeepAR, or LSTM) in production
  • Apache Kafka for ML feature pipelines
  • Proficiency in Python, PyTorch, Kubeflow

Responsibilities

  • Build PPO reinforcement learning policy using RLlib (Ray) with multi-objective reward (quality, fairness, satisfaction)
  • Develop Temporal Fusion Transformer for geo‑cell demand forecasting at city scale (7‑day horizon)
  • Integrate real‑time demand signals via Kafka time‑series pipelines
  • Create ClickHouse aggregations for demand‑supply heatmaps
  • Implement proactive alert system based on predicted demand shifts

Skills

RLlibRayPPODQNpolicy gradientTemporal Fusion TransformerTFTProphetDeepARLSTMApache KafkaPythonPyTorchKubeflowClickHouse

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