Machine Learning Engineer DLRL
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Job Summary
The ePlane Company is at the forefront of Indias urban air mobility revolution. Incubated at IIT Madras we are a deep-tech startup dedicated to designing and building the worlds most compact electric flying taxi. Our mission is to make door-to-door flying a reality drastically reducing commute times and decongesting our cities for a cleaner greener future. Were a passionate team of engineers designers and visionaries working on cutting-edge technology and were looking for brilliant minds to help us take flight.
We are looking for a person with a deep understanding of Machine Learning and Reinforcement learning to develop tools that help us an organisation engineer better systems and also build the platform software that will allow the next generation of mobility to thrive. This person will develop research and deploy ML algorithms across different engineering disciplines and build tools for routing scheduling future mobility transportation networks involving hundreds of aircraft in shared regions.
Build and maintain data pipelines for model training validation and continuous retraining
Develop training pipelines architecture and prototyping for ML/RL algorithms
Work on productising research prototypes
Conduct experiments to benchmark new techniques and evaluate model behavior
Develop systematic evaluation methodology: test sets accuracy metrics citation quality scoring false positive/negative analysis
Deploy AI tools to engineering teams with structured pilots baseline measurement and documented adoption outcomes
Build and operate multi-step agentic workflows connecting engineering data sources for reasoning
3 years ML engineering with a focus on deep learning/reinforcement learning
Strong ML stack: PyTorch or TensorFlow Pandas NumPy SciPy
Strong programming skills in Python/C
Hands on experience implementing Neural network architectures like CNNs Transformers RNNs VAEs
Optimization of DL models(Memory execution time size) for inference
Working knowledge of full life cycle and various SDLC methodologies to meet project goals
Ability to design production ML systems that fail gracefully and whose failure modes are understood and documented
Reinforcement learning experience: policy training reward engineering simulation environment construction
Gradient-based optimization
Automatic differentiation tools and development
Experience developing ML systems in Safety-critical or regulated domain background where AI output quality must be explainable
Experience building multi-step agentic workflows
Familiarity with aerospace change management processes
Required Skills:
Required Qualifications 3 years ML engineering with a focus on deep learning/reinforcement learning Strong ML stack: PyTorch or TensorFlow Pandas NumPy SciPy Strong programming skills in Python/C Hands on experience implementing Neural network architectures like CNNs Transformers RNNs VAEs Optimization of DL models(Memory execution time size) for inference Working knowledge of full life cycle and various SDLC methodologies to meet project goals Ability to design production ML systems that fail gracefully and whose failure modes are understood and documented Reinforcement learning experience: policy training reward engineering simulation environment construction Preferred Qualifications Gradient-based optimization Automatic differentiation tools and development Experience developing ML systems in Safety-critical or regulated domain background where AI output quality must be explainable Experience building multi-step agentic workflows Familiarity with aerospace change management processes
