Machine Learning Engineer - Time Series Analysis
Swasti · Bengaluru · onsite · Posted 6d ago
Not disclosed3–5 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
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
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
- 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
- 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
- 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
Sourced from foundit
Skills
Time Series AnalysiscnnsRNNsPytorchTensorflowPythonsignal processing techniques