ML Engineer vs AI Engineer vs Data Scientist: Roles & Salaries

Introduction: Why This Guide Matters

If you're preparing for machine learning interviews, you’ve probably seen job titles like "ML Engineer," "AI Engineer," or "Research Scientist" thrown around—often with overlapping descriptions. But here’s the truth: understanding the differences between ML Engineer vs AI Engineer vs Data Scientist is crucial to targeting the right role and preparing effectively.

FAANG+ companies have distinct expectations for each role.

Interview prep strategies vary drastically (a Data Scientist won’t be grilled on MLOps, but an ML Engineer will).

Transitioning between roles requires targeted upskilling (e.g., a Data Engineer moving into AI needs more than just Python).

In this guide, we’ll break down:

What each role actually does (no fluff, just real-world responsibilities).

Skills & interview questions you must prepare for.

How to transition from your current background (SWE, Data Analyst, etc.).

Let’s dive in!

Machine Learning (ML) Engineer: The "Deployment Guru"

What Does an ML Engineer Do?

ML Engineers bridge the gap between data science and software engineering. They don’t just build models—they make them scalable, reliable, and production-ready.

Day-to-Day Responsibilities:

✔ Deploying ML models using Docker/Kubernetes.

✔ Optimizing models for low latency/high throughput (e.g., pruning neural networks).

✔ Building ML pipelines (feature stores, monitoring drift).

✔ Collaborating with Data Scientists to operationalize research.

Key Skills Needed

Typical Interview Questions

Coding: "Implement a streaming feature engineering pipeline."

System Design: "How would you deploy a recommendation system for 10M users?"

Debugging: "Your model’s latency spiked in production—how do you fix it?"

Who Should Aim for This Role?

Software Engineers who enjoy infrastructure/scalability.

Data Scientists tired of "Jupyter Notebook limbo" and want to ship models.

Pro Tip: FAANG interviews focus heavily on ML system design—practice architectures like Netflix’s recommender system.

AI Engineer: The "Applied AI Specialist"

What Does an AI Engineer Do?

AI Engineers build AI-powered applications—think ChatGPT plugins, self-driving car perception, or voice assistants.

Key Differences from ML Engineers:

More focus on NLP, CV, or Generative AI.

Less emphasis on large-scale deployment (unless it’s a startup).

Day-to-Day Responsibilities:

✔ Fine-tuning LLMs (GPT, Llama 2) for specific tasks.

✔ Optimizing transformer models for edge devices.

✔ Implementing RAG (Retrieval-Augmented Generation) systems.

Key Skills Needed

Typical Interview Questions

"How would you reduce hallucinations in an LLM chatbot?"

"Implement a custom attention mechanism in PyTorch."

"Design a real-time object detection system for drones."

Who Should Aim for This Role?

ML Engineers who want to specialize in NLP/CV.

Researchers transitioning to industry (but don’t want pure academia).

Pro Tip: Start a GitHub portfolio with AI projects (e.g., "Fine-tuning Llama 2 for medical Q&A").

Data Scientist: The "Insights Storyteller"

What Does a Data Scientist Do?

Data Scientists turn raw data into actionable insights—whether it’s optimizing ad clicks, predicting churn, or running A/B tests.

Key Differences from ML Engineers:

More statistics & business focus vs. deployment.

Less software engineering rigor (but SQL/Python are a must).

Day-to-Day Responsibilities:

✔ Exploratory Data Analysis (EDA) – Finding patterns in messy data.

✔ Building predictive models (e.g., churn, recommendation systems).

✔ Designing A/B tests – Did that UI change increase conversions?

✔ Communicating insights to non-technical stakeholders.

Key Skills Needed

Typical Interview Questions

SQL: "Calculate month-over-month retention using a sessions table."

Stats: "How would you determine if a new feature increased revenue?"

Case Study: "How would you measure the success of TikTok’s For You Page algorithm?"

Who Should Aim for This Role?

Data Analysts who want to upskill in ML.

Academic Researchers (physics, economics) comfortable with stats.

Pro Tip: Product Sense is huge at FAANG—practice metrics-driven thinking (e.g., "How would you improve Netflix’s recommendation system?").

Data Engineer: The "Pipeline Architect"

What Does a Data Engineer Do?

Data Engineers build the infrastructure that powers AI/ML. Without them, Data Scientists would drown in unprocessed logs.

Key Differences from Data Scientists:

Focus on scalability, not analysis.

Heavy distributed systems knowledge.

Day-to-Day Responsibilities:

✔ Designing data warehouses (BigQuery, Snowflake).

✔ Building ETL pipelines (Spark, Airflow).

✔ Ensuring data quality (schema validation, monitoring).

Key Skills Needed

Typical Interview Questions

"How would you design a real-time fraud detection pipeline?"

"Optimize this slow SQL query."

"Compare Parquet vs. Avro for storing IoT data."

Who Should Aim for This Role?

Backend Engineers who love big data challenges.

Data Analysts tired of writing the same SQL queries.

Pro Tip: Learn Spark internals—FAANGs love asking about "shuffles" and "partitioning strategies."

Research Scientist (AI/ML): The "Algorithm Pioneer"

What Does a Research Scientist Do?

They push the boundaries of AI—think Google Brain, OpenAI, or Meta FAIR.

Key Differences from ML Engineers:

Publish papers, not ship products.

Deep math/theory focus (e.g., "Why does this optimization method converge?").

Day-to-Day Responsibilities:

✔ Reading papers (arXiv is your best friend).

✔ Proposing novel architectures (e.g., a new attention mechanism).

✔ Collaborating with engineers to test ideas at scale.

Key Skills Needed

Typical Interview Questions

"Derive the backpropagation rule for an LSTM."

"Improve this transformer architecture for long sequences."

"Explain the bias-variance tradeoff in non-convex optimization."

Who Should Aim for This Role?

PhD graduates in ML/AI.

ML Engineers who miss theoretical depth.

Pro Tip: Reimplement papers (e.g., "Attention Is All You Need")—it’s the best interview prep.

Side-by-Side Comparison Table

How to Transition into These Roles (Detailed Roadmap)

From Software Engineer → ML Engineer

Step 1: Close the Skill Gaps

Learn MLOps: Take the MLOps Zoomcamp (covers Docker, MLflow, TFX).

Master Cloud ML: Deploy a model on AWS SageMaker or GCP Vertex AI (e.g., "Predict house prices with Flask + SageMaker").

Practice System Design: Use the ML System Design Primer.

Step 2: Build a Portfolio

Project Idea: "Real-time fraud detection system with FastAPI + Kubernetes."

GitHub Must-Haves:

A Dockerized ML model.

A monitoring script (e.g., tracking data drift with Evidently).

Step 3: Network

Join MLOps.community Slack.

Contribute to open-source (e.g., Kubeflow, MLflow).

From Data Analyst → Data Scientist

Step 1: Upskill in ML/Stats

Courses:

Advanced Data Science with IBM (Coursera) (covers Spark, ML).

A/B Testing by Google.

Key Stats Concepts:

Bayesian vs. Frequentist A/B tests.

Confounder adjustment (e.g., "How to measure ad impact when seasonality exists?").

Step 2: Showcase Business Impact

Kaggle Project Example:

"Optimizing Airbnb pricing with ML: Increased host revenue by 12% in simulations."

LinkedIn Tip: Post your analysis (e.g., "Here’s how I found hidden bias in this dataset").

Step 3: Ace the Interview

SQL Drill: Practice 100+ problems on LeetCode (focus on window functions).

Case Study Framework:

Define the metric (e.g., "Click-through rate").

Brainstorm confounders (e.g., "Does time of day affect clicks?").

Propose a randomized experiment.

From Backend Engineer → Data Engineer

Step 1: Master Distributed Systems

Books:

Designing Data-Intensive Applications (Bible for DEs).

High-Performance Spark.

Hands-On:

Build a real-time pipeline (Kafka + Spark Streaming).

Optimize a slow Parquet query (use partitioning + predicate pushdown).

Step 2: Get Cloud-Certified

AWS Certified Data Analytics or Google Professional Data Engineer.

Project: "Cost-optimized data lake on S3/Redshift."

Step 3: Interview Prep

Spark Optimization Qs:

"How would you handle skew in a Spark join?" → Answer: Salting.

"When would you use broadcast vs. sort-merge joins?"

Pipeline Design: Use the "ETL vs. ELT" tradeoff framework.

From Academia → Research Scientist

Step 1: Publish or Perish

Start Small: Submit to workshops (NeurIPS ML Safety, ICML Tiny Papers).

Reproduce Papers: Blog about replicating "AlphaGeometry" or "Mistral 7B".

Step 2: Industry-Ready Skills

Code Like a Pro:

Write efficient PyTorch (avoid CPU-GPU transfers).

Use Weights & Biases for experiment tracking.

Math Drill:

Re-derive SGD convergence proofs.

Implement SOTA optimizers (e.g., AdamW from scratch).

Step 3: Nail the Interview

Paper Discussion Prep:

"Explain the key innovation in the RetNet paper."

"How would you improve it?"

Coding Test: Expect algorithmic PyTorch (e.g., "Write a custom autograd function").

How InterviewNode Can Help ?

1:1 Coaching

Ex-FAANG Interviewers: Get grilled by Meta ML Engineers or Google Research Scientists.

Customized Drills:

"Let’s simulate a Tesla Autopilot system design interview."

Study Plans

30-Day Sprints:

Week 1-2: Core theory (e.g., "Attention mechanisms").

Week 3-4: Mock interviews + gap analysis.

Resume & LinkedIn Optimization

ATS-Friendly Templates: Highlight role-specific keywords (e.g., "Kubeflow" for ML Engineers).

GitHub Portfolio Review: We’ll suggest pinned projects (e.g., "Deployed BERT model with FastAPI").

Final Thoughts

The AI/ML field is vast, but knowing these role differences ensures you:

✔ Prep efficiently (no wasted time studying MLOps for a Data Scientist role).

✔ Tailor your resume (highlight the right keywords).

✔ Nail the interview (by anticipating what’ll be asked).

Ready to ace your interviews? Register for our free webinar and find out more.