Machine Learning Lead
Posted Updated
Description
Responsibilities:
- Define and drive the ML roadmap across the product ecosystem, aligning model development with product and business priorities.
- Lead the design of custom ML models from scratch for digital signal processing, audio signal analysis, and accelerometer data, with published papers or demonstrable custom-built models expected.
- Apply advanced techniques, time series analysis, CNNs, RNNs, GANs, LLMs, and low-rank adaptation to solve hard problems in pet wellness technology.
- Build, lead, and mentor a team of ML engineers, setting technical standards and a culture of rigorous experimentation.
- Own sophisticated data analysis and preprocessing pipelines that prepare datasets for ML at scale.
- Establish MLOps practices and infrastructure that get models into production reliably, at scale.
- Set benchmarks for continuously monitoring and optimizing model accuracy and efficiency.
- Partner closely with product, engineering, and data science leadership to ship ML into Hoomanely's product ecosystem.
- Stay ahead of the field in ML and signal processing, and drive a culture of research and innovation at Hoomanely.
Requirements:
- You have a B. Tech in Computer Science, Data Science, Electrical Engineering, or a related field from a Tier 1 institute (IIT, NIT, BITS Pilani, or equivalent), which is good to have.
- You have 10+ years of experience building custom ML models, including for digital signal processing, audio signal analysis, or accelerometer data.
- You've led and grown ML teams, with a track record of taking models from research into production.
- You have strong depth in time-series ML, CNNs, RNNs, GANs, LLMs, and low-rank adaptation techniques.
- You have deep MLOps experience, deploying and running ML models in production.
- You're fluent in TensorFlow, PyTorch, and Python.
- You bring strong analytical, problem-solving, communication, and leadership skills.
Bonus:
- Experience in pet wellness or a related industry.
- Familiarity with IoT device data processing and analysis.
- Knowledge of cloud platforms for ML deployment.
- Published research or patents in applied machine learning.
