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The ideal candidate will have a strong background in applying machine learning to Digital Signal
Processing, particularly in audio signal analysis and accelerometer data analysis. This role involves
developing innovative ML models, including CNNs, RNNs, GANs, and leveraging techniques such as LLMs
and low-rank adaptation for enhancing our product offerings. The successful candidate will also be
proficient in MLOps practices and deploying ML models into production environments.
Key Responsibilities:
1. Custom ML Model Development: Design and build custom machine learning models from
scratch, tailored to specific applications in digital and audio signal processing, and accelerometer
data analysis. The ideal candidate will have published papers or demonstrable customized model
custom built for specific problem domains.
2. Advanced ML Techniques: Apply advanced machine learning techniques, including time series
analysis, CNNs, RNNs, GANs, LLMs, and low-rank adaptation, to solve complex problems in pet
wellness technology.
3. Data Analysis and Processing: Perform sophisticated data analysis and preprocessing to prepare
datasets for machine learning applications.
4. MLOps and Model Deployment: Implement MLOps practices to streamline the deployment of
machine learning models into production, ensuring scalability, performance, and reliability.
5. Performance Optimization: Continuously monitor and optimize ML models to improve accuracy
and efficiency.
6. Cross-functional Collaboration: Work closely with product development, engineering, and data
science teams to integrate ML models into Hoomanely Inc.’s product ecosystem.
7. Research and Innovation: Stay abreast of the latest developments in machine learning and
signal processing to drive innovation within the company.
Qualifications:
● Bachelors/Masters in Engineering in Computer Science, Data Science, Electrical Engineering, or a
related field with a focus on machine learning. Preferably from a top tier (Tier 1 in India/US - IIT,
NIT equivalent) institute.
● Proven experience in building custom ML models for digital signal processing, audio signal
analysis, and accelerometer data analysis. Minimum 6 years of relevant experience.
● Strong knowledge of time series-based machine learning, CNNs, RNNs, GANs, LLMs, and
low-rank adaptation techniques.
● Experience with MLOps practices and deploying machine learning models in production
environments.
● Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming
languages (e.g., Python).
● Excellent analytical, problem-solving, and communication skills.
Preferred Skills:
● Experience in pet wellness or related industries.
● Familiarity with IoT device data processing and analysis.
● Knowledge of cloud computing platforms and services for ML deployment.
