Machine Learning Engineer - Time Series Analysis

About The Role

We're looking for a hands-on ML engineer to build and ship models across our sensor-driven product ecosystem, working closely with the ML Lead on digital signal processing, computer vision, and time series problems. You're comfortable taking a model from an early prototype through to a reliable, production-deployed system.

What You'll Do

• Build and iterate on ML models for time series, audio signal, and sensor fusion problems, under the guidance of the ML Lead
• Build and maintain data preprocessing and feature engineering pipelines that prepare sensor data for training and inference at scale
• Package and deploy models into production, including on edge or embedded compute, with attention to latency and resource constraints
• Run structured experiments to evaluate model accuracy, generalization, and failure modes, and document findings clearly
• Support monitoring and retraining pipelines that track model performance and drift after deployment
• Work closely with firmware, hardware, and app teams to turn model outputs into clear, actionable product insight

What We're Looking For

• B.Tech in Computer Science, Electrical Engineering, or related field from a Tier 1 institute (IIT, NIT, BITS Pilani, or equivalent) - mandatory
• 3+ years of applied machine learning experience
• Solid understanding of time series analysis and at least one of CNNs, RNNs, or signal processing techniques
• Proficiency in Python and at least one ML framework (TensorFlow or PyTorch)
• Experience taking at least one model from prototype to a production or near-production deployment

Bonus Skills

• Experience with audio signal processing or accelerometer-based activity classification
• Familiarity with edge deployment of ML models on embedded compute
• Exposure to MLOps tools and practices
• Prior experience in IoT, wearables, or health-tech

Why Join Us

• Ship models that run on real hardware, on real dogs - not just in a notebook
• Learn directly from the ML Lead while owning real pieces of the roadmap
• Take ownership early, with a direct line to the people building the rest of the product
• Join a small, fast-moving team building a category-defining, sensor-driven product

(ref:hirist.tech)