Amazon·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Amazon ML Engineer technical screen, pretty much a deep dive into reinforcement learning fundamentals. One meaty question that took up most of the time.

Questions Asked (1)

Walk me through the core components of a reinforcement learning setup: what makes up the underlying Markov decision process, what additional pieces does the agent itself define, and how does everything connect in the actual learning loop?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

This felt broader than I expected for a phone screen.

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AI HintsAI Generated

Suggested Approach

Structure your answer by first defining the MDP components (states, actions, rewards, transitions, discount factor), then explaining the agent's additional components (policy, value function, model), and finally describing how they interact in the learning loop (interacting with environment, collecting experience, updating policy/value). Use a concrete example like a robot navigating a grid to illustrate.

Pro tip: Emphasize the distinction between the environment (MDP) and the agent, and highlight how the learning loop iteratively improves the policy based on feedback—this shows you understand the core of RL and can communicate it clearly.

1. Define the MDP

Explain that the Markov Decision Process consists of states, actions, transition probabilities, rewards, and a discount factor. Mention that it formalizes the environment.

2. Describe the agent's components

Outline the policy (mapping states to actions), value function (estimating expected return), and optionally a model of the environment. These are what the agent learns or uses.

3. Explain the learning loop

Walk through the iterative process: the agent observes a state, selects an action via its policy, receives a reward and next state from the environment, then updates its policy/value estimates based on this experience.

4. Connect components and highlight goal

Summarize how the MDP and agent components interact to maximize cumulative reward, and mention that learning involves balancing exploration and exploitation.

Key Points to Mention

Markov property: future depends only on current state and action
Policy: deterministic or stochastic mapping from states to actions
Value function: state-value V(s) and action-value Q(s,a)
Exploration vs exploitation trade-off (e.g., epsilon-greedy)
Model-free vs model-based RL
Discount factor and its role in balancing immediate and future rewards

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.