Role : Machine Learning Lead

Full-Time | Bengaluru, Work from Office

Qualifying Degree : B.Tech from a Tier 1 Institute | Experience : 10+ years

Note : Shortlisted candidates will complete a take-home assignment as part of the process.

About Us :

At Hoomanely, we're building the first precision health platform for pets - a sensor-driven, AI-powered ecosystem that decodes the health signals dogs give off every day, the ones most people miss, and turns them into insight you can act on.

It starts with EverBowl, a clinical-grade device that reads hydration, intake, body temperature, eating behavior, and the sound of a bite - building toward a single daily wellness score. Paired with the Hoomanely app and Protect+, our insurance-rewards membership, we're helping pet parents move from guesswork to knowing. Calm. Confident. In control.

We're not building another pet gadget. Every signal we decode - acoustic, motion, thermal - depends on machine learning done right. That's the problem this role exists to solve.

About the Role :

We're looking for an experienced ML leader to drive the machine learning roadmap across Hoomanely's sensor-driven product ecosystem, with deep focus on digital signal processing, audio signal analysis, and accelerometer data. You've led ML teams and taken models from research to production before - building CNNs, RNNs, GANs, LLMs, and low-rank adaptation techniques into real products. You'll set technical direction, grow a team of ML engineers, and turn raw sensor data into the insight that helps pets live longer, healthier lives.

What You'll Do :

- 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 - published papers or demonstrable custom-built models expected

- Apply advanced techniques - time series analysis, CNNs, RNNs, GANs, LLMs, 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

You Might Be a Fit If :

- You have a B.Tech in Computer Science, Data Science, Electrical Engineering, or related field from a Tier 1 institute (IIT, NIT, BITS Pilani, or equivalent) - mandatory

- You have 10+ years 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

- Bonus : familiarity with IoT device data processing and analysis

- Bonus : knowledge of cloud platforms for ML deployment

- Bonus : published research or patents in applied machine learning

Why Join Us :

- Lead ML from the ground up on a category-defining, sensor-driven product - not a legacy stack

- Own one of the hardest, most interesting problems in pet health : turning raw signal into insight

- Take full ownership from day one, with a direct line to product and founding leadership

- Build the team you wished you'd had - hire, mentor, and set the bar

To Apply :

Share your background, along with any published papers, custom models, or production ML systems you've built. Shortlisted candidates will complete a short assignment as part of our process.