Applied Scientist I

Round 1: Online Assessment (coding & ML concepts) → Round 2: Technical Phone Screen (algorithmic coding, system design, ML fundamentals) → Round 3: Onsite Interviews (multiple back‑to‑back rounds covering coding, ML research, design, and behavioral "Leadership Principles") → Round 4: HR Discussion (compensation, fit, and next steps)

Amazon promotes a high‑performance culture driven by its Leadership Principles, encouraging ownership, bias for action, and continuous learning. Employees benefit from clear career ladders, mentorship programs, and exposure to global projects, while work‑life balance is supported through flexible work arrangements and robust internal resources.

Amazon’s interview process is rigorous, typically starting with an online coding assessment followed by a technical phone screen that tests data structures, algorithms, and core ML concepts. Successful candidates are invited for onsite rounds consisting of 4‑5 back‑to‑back interviews covering deep‑learning research, system design, coding, and behavioral questions based on Amazon’s Leadership Principles. Preparation should focus on solving medium‑hard coding problems, articulating research contributions, and demonstrating a customer‑obsessed mindset.

B.Tech/M.Tech/Ph.D. in Computer Science, Electrical Engineering, Mathematics, or related fields; minimum CGPA 7.0/10 (or equivalent); graduation batch 2025‑2027; no active backlogs; strong foundation in machine learning, deep learning, and statistical modeling; research publications in top conferences are a plus.

Amazon, founded in 1994, has grown into one of the world’s most valuable technology companies, operating across e‑commerce, cloud computing, digital streaming, and artificial intelligence. In India, Amazon employs over 100,000 people and runs massive fulfillment networks, AWS data centers, and research labs that power services for millions of customers. The company’s culture emphasizes customer obsession, innovation, and long‑term thinking, offering employees a fast‑paced environment where ideas are turned into products that impact billions of users worldwide. The Applied Scientist I role sits within Amazon Web Services’ AI/ML team, focusing on Speech and Language technologies such as Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Translation (MT), Text‑to‑Speech (TTS) and Dialog Management. As a fresh graduate, you will work alongside senior researchers and engineers to design, prototype, and scale novel algorithms that improve the accuracy, latency, and robustness of Amazon’s voice‑enabled services like Alexa, Amazon Transcribe, and Amazon Translate. The position offers hands‑on exposure to massive heterogeneous data sets—audio, text, and structured logs—while leveraging Amazon’s world‑class compute infrastructure. Key responsibilities include: 1. Conduct literature reviews and translate state‑of‑the‑art research into production‑ready models for speech and language tasks. 2. Design and implement deep learning architectures (e.g., Transformers, Conformer, RNN‑Transducer) using frameworks such as PyTorch or TensorFlow. 3. Develop data pipelines for large‑scale audio and text preprocessing using Spark or AWS Glue. 4. Optimize model training and inference pipelines for cost‑effective, low‑latency deployment on AWS services. 5. Perform rigorous experiments, statistical analysis, and A/B testing to validate model improvements. 6. Collaborate with product managers, software engineers, and UX designers to integrate research outcomes into customer‑facing features. 7. Publish findings in internal forums and, where appropriate, external conferences (NeurIPS, ICML, ACL). 8. Contribute to open‑source tooling and internal libraries that accelerate AI research across Amazon. 9. Mentor interns and junior team members, sharing best practices in ML engineering. 10. Stay updated with emerging trends in speech, NLP, and computer vision to continuously push the technology frontier. Tech stack: Python, PyTorch/TensorFlow, AWS SageMaker, Spark, Hadoop, Docker/Kubernetes, Git, C++ (optional for performance‑critical components), and familiarity with large‑scale distributed training techniques. The role offers a clear growth path—from Applied Scientist I (L5) to Senior Applied Scientist (L6) and Principal Applied Scientist (L7)—with opportunities to lead cross‑functional projects, influence product strategy, and contribute to Amazon’s broader AI roadmap. Joining Amazon provides access to world‑class mentorship, a culture of relentless innovation, competitive compensation, and the chance to see your research directly impact products used by millions every day.