Amazon India ML Engineer vs Applied Scientist: 2026 Fresher Guide

Amazon India runs two distinct fresher paths into AI work: the SDE-1 route for B.E./B.Tech graduates and the Applied Scientist route that almost always requires an MS or PhD.

Both roles exist within Amazon India’s engineering and science teams. Both work on real ML systems. But they are not interchangeable entry points, and most freshers looking at Amazon job postings mistake one for the other. This article separates them clearly.

Two Entry Points, Not One

When you search for “Amazon India ML jobs 2026”, you will find two role families: Software Development Engineer (SDE-1) and Applied Scientist (AS-I). They sit in the same organisation, sometimes on the same team. The degree requirements and interview bars are fundamentally different.

• SDE-1 (ML Engineer path): Open to B.E./B.Tech freshers in CSE, IT, ECE, and related fields with a 60% aggregate or above. This is the realistic entry for most engineering graduates.
• Applied Scientist I (research path): Designed for candidates with an MS or PhD in machine learning, statistics, operations research, or a related discipline. Fresh B.E./B.Tech graduates are very rarely hired directly into AS-I roles.

The SDE-1 who joins an ML team (Alexa NLU, Amazon Ads ML, supply chain optimisation, fraud detection, or AWS Applied AI) is doing engineering work: writing production code, building ML pipelines, improving model serving infrastructure. The Applied Scientist on the same team is doing research work: designing experiments, selecting and tuning model architectures, reviewing findings. Different skills, different interview expectations.

The SDE-1 (ML Engineer) Track

The SDE-1 fresher track is Amazon India’s main engineering hiring pipeline. According to TechGig’s May 2026 reporting on Amazon’s off-campus drive, Amazon ran multiple SDE-1 drives in 2025 and 2026 targeting the 2024 and 2025 graduating batches, with roles across Bangalore, Hyderabad, and Chennai.

Eligibility

• Degree: B.E./B.Tech or M.E./M.Tech in Computer Science, Information Technology, ECE, or a related field
• Aggregate: 60% or above across graduation
• Languages: proficiency in at least one of Java, Python, or C++
• DSA: strong data structures and algorithms fundamentals

What SDE-1 Engineers Do on ML Teams

Fresh SDE-1 hires at Amazon India do not design model architectures on day one. The work is engineering-heavy: building data pipelines, deploying models to production, writing the backend that serves ML predictions at scale, and improving the reliability of existing ML systems. With time and demonstrated ML judgment, the scope expands well beyond the initial engineering tasks.

Amazon India ML teams that SDE-1 freshers may join include:

• Alexa NLU (natural language understanding for voice)
• Amazon Ads ML (click-through and conversion modelling)
• Supply chain and fulfillment ML (demand forecasting, inventory allocation)
• Fraud detection systems
• Applied AI in Amazon Web Services

Selection Process for SDE-1

See Amazon’s full recruitment process breakdown for detailed round-by-round guidance. The sequence for SDE-1 freshers:

1. Online Assessment: 2 coding problems in 90 minutes
2. Technical interviews: 2 to 3 rounds covering DSA, system design basics, and Amazon Leadership Principles
3. Bar-Raiser round: an independent senior interviewer who assesses outside the hiring team’s influence
4. HR round

All 14 Amazon Leadership Principles are assessed across every interview round, not just HR. “Customer Obsession”, “Are Right, A Lot”, and “Dive Deep” come up in technical rounds through behavioural questions. Answers that connect a technical decision to a Leadership Principle score higher than answers describing only the technical choice.

Amazon WoW: The Women Engineers Track

Amazon WoW (Women of the World) is Amazon’s dedicated program for women engineers and students. The selection process mirrors the standard SDE-1 hiring sequence exactly, with structured mentoring and networking support added on top. The program is open to women candidates from Tier-2 and Tier-3 colleges across Bangalore, Hyderabad, and Chennai.

The Applied Scientist Track

The Applied Scientist Intern and AS-I FTE tracks operate differently. The entry pipeline for research-focused students is the Amazon ML Challenge, an online competition that Amazon runs periodically. Strong performers are considered for Applied Scientist Intern roles. Per the Applied Scientist Intern listing via Prosple, the stipend in India sits in the range of ₹1,50,000 to ₹2,50,000 per month (aggregator estimate; Amazon does not officially disclose compensation figures). Successful internships may convert to Applied Scientist I FTE offers.

Who the AS Track Is Actually For

The selection sequence for Applied Scientist candidates includes:

• Amazon ML Challenge performance or research portfolio review
• Technical interviews covering ML theory, coding, and research presentation
• HR round

The MS or PhD requirement is real, not advisory. Amazon’s Applied Scientist teams publish research and build novel ML systems. The interview expects candidates who have run experiments, understand the mathematics behind model selection, and can defend research decisions. A B.E./B.Tech fresher without publication or serious research project experience is not competitive here.

The straightforward framing: if your degree is B.E./B.Tech and you want AI work at Amazon India, target SDE-1 on an ML team. The Applied Scientist path is for after an MS or PhD.

Selection Process Comparison

Dimension	SDE-1 (ML Engineer path)	Applied Scientist Intern
Minimum degree	B.E./B.Tech in CSE, IT, ECE	MS or PhD in ML, statistics, or related
Entry mechanism	Off-campus drives via amazon.jobs	Amazon ML Challenge then research review
Online assessment	2 coding problems, 90 minutes	ML Challenge competition
Technical interviews	DSA, system design, Leadership Principles	ML theory, coding, research presentation
Bar-Raiser round	Yes	Yes
CTC or stipend	28 to 32 LPA, Bangalore or Hyderabad (aggregator estimate; not officially disclosed)	₹1,50,000 to ₹2,50,000 per month internship stipend (aggregator estimate; not officially disclosed)
Locations	Bangalore, Hyderabad, Chennai	Bangalore, Hyderabad
FTE entry	SDE-1 from day one	Internship to AS-I FTE conversion

Positioning for the SDE-1 ML Role from Tier-2 and Tier-3 Colleges

Amazon India does not maintain a campus-exclusive list that shuts out Tier-2 and Tier-3 graduates. The off-campus drives that TechMonk and the Economic Times reported in 2025 and 2026 as a growth hiring position were open applications, not campus-restricted. The off-campus route is where the Tier-2 and Tier-3 window sits.

What determines whether you clear the OA:

• Solving at least one of the two problems fully within 90 minutes
• Clean, correct code with good edge-case handling
• Time and space complexity awareness in your solution

What determines whether you clear the technical rounds:

• DSA fundamentals (linked lists, trees, graphs, dynamic programming, sorting)
• Ability to walk the interviewer through your thinking, not just your answer
• One or two projects you built end-to-end and can speak to in depth

The Leadership Principles requirement gives Tier-2 and Tier-3 candidates a real opportunity. A candidate from a less-recognised college who has an ML project they built and can explain the decisions they made will interview better than a candidate who has memorised Leadership Principle definitions but has no concrete examples attached to them.

See how competitive coding builds interview readiness for the practice habit that moves candidates from comfortable with easy problems to ready for Amazon OA difficulty. The 2026 OA pattern update covers what changed in the assessment format this year.

Building ML Depth Before and After You Join

The SDE-1 offer gets you into Amazon India. Getting onto an ML team, and staying relevant on that team, requires something the OA does not test: ML judgment built from shipping something real.

An SDE-1 who joins a non-ML team can request an internal transfer to an ML-focused team after demonstrating the skills. Amazon’s internal mobility system makes this genuinely available, not a theoretical option. The SDE-1s who make that move kept building ML skills throughout their first year. They also had a specific project or contribution ready to show when they applied internally.

The 2026 AI roadmap for Indian engineering students covers what that ML depth looks like as a structured curriculum: what to learn, in what order, and how to fit it into a placement timeline.

If you are working through that roadmap and want a concrete, deployable output to show at an Amazon interview, TinkerLLM is where to build it. The SDE-1 OA is coding-first, but the technical interviews probe ML intuition. A ₹299 subscription gives you real LLM API calls and a working micro-project, which turns the Leadership Principles question “tell me about a time you dove deep into a technical problem” into something you can answer with a specific, shipped artefact rather than a hypothetical.