Senior Machine Learning Researcher

3 Months ago • 5-8 Years • Artificial Intelligence • Undisclosed

About the job

Job Description

Senior Machine Learning Researcher needed for deep tech company SatSure, working on earth observation data for agriculture, infrastructure, and climate action. Must-haves: deep learning, statistical modeling, Python, ML frameworks (TensorFlow/PyTorch).
Must have:
  • Deep Learning
  • Statistical Modeling
  • Python Expertise
  • ML Frameworks
Good to have:
  • Earth Observation
  • Data Product Research
  • Model Monitoring
  • Active Learning
Perks:
  • Medical Health Cover
  • Skill Development

About the job

Seeking an experienced machine learning professional with expertise in deep learning and statistical modeling for earth observation data applications. Key responsibilities include model development for segmentation, object classification, crop monitoring, and disaster management. Proficiency in Python, ML frameworks (TensorFlow, PyTorch), and strong research skills are essential. Advanced degree required.


About SatSure:


SatSure is a deep tech, decision Intelligence company that works primarily at the nexus of agriculture, infrastructure, and climate action creating an impact for the other millions, focusing on the developing world. We want to make insights from earth observation data accessible to all.


Join us to be at the forefront of building a deep tech company from India that solves problems for the globe.


Roles and Responsibilities:


  • Implementing deep-learning models and/or frameworks that can be used in scene/semantic/panoptic segmentation, object classification, synthetic data generation using earth observation data in agriculture and infrastructure domain.
  • Design and implement performance evaluations frameworks for incremental and step function improvements.
  • Exploring and developing new methods for improving detection/classification performance
  • Building model monitoring and active learning pipelines to address the target and data shifts happening in the domain.
  • Building models to solve open research problems on LULC, crop classification, change monitoring and disaster management etc from earth observations.
  • Define and implement performance metrics for the research problems and be very methodical / organized with experiment management and performance evaluations.



Qualifications:


  • Master’s degree or PhD in Engineering, Statistics, Mathematics or Computer Science, Machine Learning / Deep Learning domains
  • 5-8 years of experience in machine learning based data product research and development in addition to relevant educational background in the field of Computer Science, Engineering, Machine Learning etc.


Must-Haves:


  • Experience with tools/frameworks from theoretical and practical aspects of computer science and ML/DL. This includes probability, statistics, learning theory etc.
  • Prior experience working with statistical modeling techniques and deep learning fundamentals, such as CNN, RNN, Transformers, GANs etc include sequence networks eg LSTMs
  • Proficiency in any programming language, preferably Python, with hands-on experience in using ML frameworks such as Tensorflow/Pytorch, Pytorch Lightning, MxNet etc
  • Ability to design, develop, optimize for machine learning/ deep learning models
  • Knowledge of application of ML for earth observation data, is good to have
  • Appetite for research and implementing innovative solutions for complex problems


Benefits:


  • Medical Health Cover for you and your family including unlimited online doctor consultations
  • Access to mental health experts for you and your family
  • Dedicated allowances for learning and skill development
  • Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves
  • Twice a year appraisal


Interview Process:


  • Intro call
  • Assessment
  • Presentation
  • Interview rounds (ideally up to 3-4 rounds)
  • Culture Round / HR round
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About The Company

Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

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