Machine Learning Engineer - DL/RL

The ePlane Company is at the forefront of India's urban air mobility revolution. Incubated at IIT Madras, we are a deep-tech startup dedicated to designing and building the world's 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. We're a passionate team of engineers, designers, and visionaries working on cutting-edge technology, and we're 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