Applied Scientist I

Amazon's interview process typically consists of 4‑5 rounds, starting with an online coding test followed by technical phone screens and onsite sessions. Candidates face a mix of data‑structure coding, system design, and machine‑learning case studies, along with behavioral questions based on Leadership Principles. Preparation should focus on writing clean, optimized code and articulating the reasoning behind design choices.

Amazon promotes a fast‑paced, data‑driven environment where ownership and customer obsession are core values. Employees enjoy abundant learning resources, clear career ladders, and the chance to work on high‑impact projects across diverse domains.

B.Tech / B.E. / M.Tech / M.E. / Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, Mathematics or related fields. Minimum CGPA 7.0/10 (or 60%). Graduation batch 2024, 2025 or 2026. No active backlogs at the time of application. Strong foundation in algorithms, data structures, and machine learning concepts.

Round 1: Online coding assessment (Data structures & algorithms) → Round 2: Technical phone screen (ML fundamentals and coding) → Round 3: Onsite interview (multiple technical rounds covering research design, system design, coding, and Amazon Leadership Principles) → Round 4: HR discussion (fit, compensation, and next steps)

Amazon is a global technology leader that started as an online bookstore and has grown into a diversified conglomerate spanning e‑commerce, cloud computing (AWS), digital streaming, and artificial intelligence. In India, Amazon operates one of the largest e‑commerce platforms, a fast‑growing AWS data centre ecosystem, and a vibrant AI research community that powers voice assistants, recommendation engines, and logistics automation. The company’s culture is built around customer obsession, long‑term thinking, and a relentless focus on innovation, making it an attractive destination for fresh talent who want to work on products that impact millions of users worldwide. The role of Applied Scientist I is positioned within Amazon Web Services’ AI team, where scientists collaborate with software engineers, data engineers, and product managers to advance speech and language technologies. As an entry‑level researcher, you will contribute to the design, implementation, and evaluation of novel algorithms in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Translation (MT), Text‑to‑Speech (TTS), and Dialog Management. Your work will directly influence Amazon Alexa, Transcribe, and other customer‑facing services. Key responsibilities: - Design and prototype state‑of‑the‑art machine‑learning models for speech and language tasks. - Conduct large‑scale experiments on heterogeneous data (audio, text, structured logs) using AWS compute resources. - Optimize model training and inference pipelines for latency and cost efficiency. - Publish research findings in top conferences or internal Amazon forums. - Collaborate with cross‑functional teams to integrate research prototypes into production services. - Develop evaluation metrics and benchmark suites to track progress against industry standards. - Mentor interns and junior team members on best practices in ML research. - Contribute to open‑source tools or internal libraries that accelerate AI development. - Stay updated with the latest advancements in deep learning, reinforcement learning, and computer vision as they relate to language technology. Tech stack: Python, PyTorch, TensorFlow, JAX, AWS SageMaker, Spark, Hadoop, Docker, Kubernetes, Git, and common NLP libraries such as Hugging Face Transformers. Growth path: Successful contributors can progress from Applied Scientist I to Applied Scientist II, Senior Applied Scientist, Principal Scientist, and eventually Distinguished Scientist, with increasing responsibility for leading research agendas and influencing product strategy. Why join Amazon? You will work on problems that affect billions of customers, have access to world‑class compute infrastructure, and be part of a culture that rewards curiosity and data‑driven decision making. The role offers a clear technical ladder, mentorship from senior researchers, and the opportunity to publish both internally and externally, accelerating your career in AI research.