Job Description:
At NeuRelic Labs, we don’t just build models — we build real-time intelligence systems that move with the markets. As a Machine Learning Engineer, you’ll bridge the gap between cutting-edge AI research and scalable production pipelines. From transforming raw trading data into actionable features to deploying inference-ready models for our flagship platforms, you’ll play a crucial role in delivering precision, speed, and automation to the financial edge.
You’ll collaborate closely with researchers, backend engineers, and product designers to architect, deploy, and monitor ML models across our growing stack.
Responsibilities:
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Build and maintain robust ML pipelines: data preprocessing, model training, evaluation, and deployment
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Work with large-scale datasets from brokers, markets, and customer interactions to extract meaningful patterns
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Translate ML research (e.g., from our AI Research team) into production-ready code using frameworks like PyTorch, TensorFlow, or scikit-learn
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Optimize model latency, accuracy, and resource efficiency for real-time use cases in trading and analytics
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Collaborate with product teams to align technical implementation with business goals and compliance guidelines
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Monitor and retrain models based on drift, usage patterns, and updated datasets
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Participate in model review, documentation, and continuous improvement cycles
Preferred Qualifications:
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Bachelor's/Master’s degree in Computer Science, Data Science, or related field
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2–5 years of experience in building and deploying machine learning models in production
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Strong Python skills; familiarity with ML Ops tools (MLflow, Weights & Biases, Airflow, etc.)
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Experience with time series forecasting, anomaly detection, or NLP preferred
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Exposure to trading data, fintech products, or real-time analytics is a plus
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Comfort with version control, containerization, and cloud-native workflows (AWS/GCP)
