Machine Learning Scientist I

Lytx

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Bangalore, Karnataka, India (Office)2 weeks ago
LocationBangalore, Karnataka, India (Office)
QualificationBachelor’s or Master’s degree in CS, EE, ECE, or related field

Job Description

  • You will utilize frameworks like PyTorch or TensorFlow to implement and debug robust DL models, optimizing them for cloud and edge deployment using large-scale field data. Furthermore, you will leverage advanced GenAI tools and LLM APIs to build innovative RAG pipelines and AI agents for workflow automation, communicating your technical findings to internal cross-functional teams.

Key Responsibilities

  • End-to-End Development: Formulate problems, engineer data, prototype, train, evaluate, and iterate ML/DL models from scratch.
  • Model Implementation: Implement, debug, and continuously improve deep learning models using PyTorch or TensorFlow.
  • Deployment & Optimization: Optimize and deploy models effectively on cloud infrastructures and edge devices.
  • GenAI Integration: Leverage GenAI tools and LLM APIs (e.g., Claude, OpenAI, Gemini) to build solutions for internal and customer-facing use cases.
  • Workflow Automation: Build and iterate on advanced prompt engineering workflows, RAG pipelines, and LLM-powered applications alongside AI agents.
  • Cross-Functional Collaboration: Clearly communicate data findings, model evaluations, and technical trade-offs to internal engineering and product teams.

Skills & Eligibility

  • Education: Bachelor’s or Master’s degree in Computer Science (CS), Electrical Engineering (EE), Electronics & Communication (ECE), or a related technical field.
  • Experience: 0–2 years of experience in ML or DL. (Relevant internships, research, or rigorous academic project work are fully considered).
  • Frameworks: Hands-on, demonstrable experience with at least one major DL framework (PyTorch or TensorFlow).
  • Programming: High proficiency in Python; familiarity with C/C++ is highly preferred.
  • Mathematical Foundation: Strong foundational knowledge in mathematics, probability, statistics, and algorithm design.
  • ML Concepts: Solid understanding of core ML concepts including supervised/unsupervised learning, model evaluation, and regularization.
  • Bonus Assets: Experience with model deployment tools (ONNX, TensorRT, Docker), cloud ML platforms, and AI coding assistants like GitHub Copilot or Claude Code.
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