Module Overview and Job Landscape

In one sentence: this module breaks down "going from knowing how to build models to landing a deep learning job offer" into an actionable four-step pipeline — first understand what roles the market has and what skills they require, then compare against your current baseline to find gaps, close those gaps and write your capabilities into a resume, and finally verify yourself using the interview question bank.

This module has five pages (including this one). The first four correspond to the four stages of the pipeline, and the fifth page, Interview Question Bank, spans both the "fill gaps" and "prove yourself" stages. Below is the global map, followed by the job landscape and methods for choosing a direction.

1. Why a Career Module

The knowledge-learning side of deep learning (the guide, concepts, and case-studies sections of this handbook) addresses "can you do it?" — but career preparation solves a completely different set of problems:

• What exactly does the market want? The same label "algorithm engineer" looks completely different on a CV engineer's JD versus a large-language-model engineer's;
• Which role matches my background? Someone with strong math but weak engineering should apply for different roles than someone with strong engineering but few publications;
• How should I write my resume to pass the initial screen? HR and front-line engineers look at resumes from completely different angles;
• What exactly are interviewers testing? Many "interview questions" seem like knowledge checks, but they're really testing whether you've independently solved problems.

These questions are interlinked. Answering any one of them in isolation will lead you astray. So this module emphasizes the overall pipeline rather than isolated tactics.

Timeliness note: This page and the JD List contain time-sensitive content including salary and job demand data, current as of 2026-08. Verify before citing.

2. The Pipeline: Four Steps

1. See the market (this page + JD List)

Answer three questions: What are the broad role categories? What do the JDs look like for each? What are the salary and degree thresholds? The deliverable is "a shortlist of candidate roles" — usually 2–3 directions is enough; don't cast too wide a net.

2. See yourself (JD Knowledge Map)

Copy the skill keywords from the JDs, then tag each one as "can do / sort of / can't do" for yourself, producing a personal study plan. This step tells you exactly how far your current knowledge is from the target role.

3. Package yourself (Resume: What to Highlight)

Translate your study plan into resume language: rewrite each project using the "context — approach — results — reflection" four-part format, quantify outcomes, and highlight the skill combination that matches the target role. A resume is not a chronological list of experiences — it's "a chain of evidence mapped to the JD."

4. Prove yourself (Interview Question Bank)

Self-assess using the categorized question bank, upgrading from "know the answer" to "can explain it clearly on the spot." The bank is organized across seven dimensions — fundamentals, architectures, training, math, engineering, LLMs, and open-ended questions — with answer key points and linked pages for targeted practice on weak areas.

3. Job Landscape: 9 Role Categories at a Glance

The table below is a broad classification of deep learning–related roles. A few notes:

• Salaries are rough estimates for tier-1 Chinese internet/big-tech companies and overseas tech firms, covering total annual compensation including stock and other variable components. Figures vary widely — data is current as of 2026-08, so verify before citing;
• Since 2023, LLM-related roles (LLM Engineer, AI Infra) have seen significant salary premiums, but the bar is also higher;
• At the same company, an "Algorithm Engineer" may span multiple rows of this table. Always read the JD — it matters more than the job title.

Role	Typical Responsibilities	Core Skills	Salary Reference (rough)
Deep Learning Researcher	Publish papers, conduct exploratory experiments, push technical boundaries	Math foundation, PyTorch, experimental design, paper reading	Domestic: ¥500K–1.5M/year; Overseas Research Scientist: $200K–400K/year
Algorithm Engineer	Business-facing model deployment, end-to-end modeling to production	Modeling, feature engineering, evaluation, online metric analysis	Domestic: ¥350K–900K/year
CV Engineer	Deploy visual tasks: classification, detection, segmentation, OCR	CNNs, detection/segmentation frameworks, data annotation pipelines, deployment	Domestic: ¥300K–800K/year
NLP Engineer	Text understanding, generation, search, dialogue systems	Transformers, tokenization, fine-tuning, RAG, evaluation	Domestic: ¥350K–850K/year
LLM Engineer	LLM fine-tuning, alignment, RAG, agent development and evaluation	LLM fine-tuning (LoRA), prompt engineering, evaluation, inference optimization	Domestic: ¥450K–1.2M/year, higher at top firms
ML Engineer (MLE)	Turn models into reliable production services	Engineering, distributed training, MLOps, monitoring and alerting	Domestic: ¥400K–1M/year; Overseas: $180K–350K/year
AI Infra Engineer	Training/inference frameworks, cluster scheduling, operator optimization	CUDA, distributed systems, performance tuning	Domestic: ¥500K–1.3M/year, closely tied to low-level system skills
Data Scientist	Combine business analysis with modeling for decision support	Statistics, SQL, feature engineering, causal inference	Domestic: ¥300K–700K/year
Data/Annotation Engineer	Build datasets, quality control, data pipelines	Data processing tools, annotation platforms, quality assurance	Domestic: ¥200K–450K/year

How to read this table

• CV / NLP / LLM / ML Engineer are the "modeling roles" in this handbook, suitable for people who want to work on models long-term;
• AI Infra / ML Engineer are "engineering roles", suitable for strong coders interested in systems;
• Data Scientist leans toward business analysis, suitable for people with strong statistics backgrounds who enjoy working with business stakeholders;
• Job titles are just the entry point. The skill list in the JD is the real classification standard — the next section gives a three-dimensional judgment method.

4. Three-Dimensional Decision Checklist: Background, Interest, Trends

Don't pick a direction solely based on salary rankings. Score each candidate role on the three dimensions below (1–5). Only roles scoring ≥3 on all three dimensions deserve to be on your shortlist.

Dimension 1: Background

• Degree and publications: PhD holders or those with first-author top-tier paper publications have the biggest advantage for "Deep Learning Researcher" or big-tech research roles. Master's students with few publications should consider engineering roles (ML / AI Infra) as more realistic.
• Hard-skill baseline: Strong CUDA/distributed systems/C++ skills → AI Infra; strong math/statistics → Data Scientist; lots of PyTorch modeling experience → Algorithm Engineer.
• Past project types: Vision projects first → CV; text/search projects first → NLP; used open-source LLMs for applications → LLM engineering.

Honestly assessing your background helps you avoid the pitfall of "JDs that look tempting but your resume will definitely get rejected." For resume writing, see the capability-matching section.

Dimension 2: Interest

Ask yourself three questions, with answers specific enough to give examples:

1. Would I rather grind the same paper for two weeks straight, or tune the latency of an online service for two weeks?
2. Do I prefer the excitement of "getting better metrics" or the peace of mind of "the system runs stably for a month"?
3. When faced with a new task, is my first instinct to search for papers or to search for framework documentation?

Choosing the former in Q1 leans toward research/algorithms, the latter toward engineering/infra. There's no right or wrong, but you must be honest — picking the wrong direction will haunt you with mundane frustrations within a year of starting. For knowledge-base recommendations, start with Deep Learning System Anatomy to figure out which layer excites you most.

Dimension 3: Trends

Broad assessment (data current as of 2026-08, verify before citing):

• LLM-related: Fine-tuning, RAG, agent, evaluation, and inference-optimization roles continue growing, but supply is also increasing rapidly, raising the bar;
• Multimodal: The boundary between CV and NLP is further blurred by unified models, reducing the share of single-modality-only roles;
• AI Infra: Under compute scarcity, training/inference engineering roles have sustained high demand and are hard to replace quickly;
• Data and evaluation: As model capabilities converge, data quality and evaluation systems become the differentiator, increasing the value of related roles.

Use trends to "avoid picking a shrinking direction," not to "chase the hottest direction despite having zero background" — applying for a hot-role job without matching background will fail at the application stage.

5. Using Map for the Module's Four Pages

Page	Deliverable	Estimated Time	Prerequisite
Module Overview & Job Landscape	2–3 candidate roles	Half a day	Read the concepts fundamentals of this handbook
JD List	Skill keyword list per role	1–2 days	Complete the three-dimensional scoring on this page
JD Knowledge Map	Personal study plan and order	Half a day	JD skill keyword list
Capability Match: What to Highlight in Your Resume	Rewritten resume	2–3 days	At least one complete project (reference portfolio projects)
Interview Question Bank	Self-assessment records and weak-area list	1–2 weeks ongoing	Can start anytime while studying

A common mistake

Don't skip steps 1 and 2 and jump straight into resume writing. Without the "skill keyword list" as an anchor, your resume easily becomes "what I've studied" rather than "what JD requirements I can fulfill." Spend two days breaking down the JDs first — your resume and interview preparation efficiency will double afterward.

6. Connecting to the Rest of the Handbook

The career module is not an isolated silo. It translates the entire handbook's "learning" into "selling":

• Knowledge base: The concepts section's Neural Network Fundamentals, Optimization & Gradient Descent, and Backpropagation & Automatic Differentiation are foundational knowledge tested in virtually every role's interviews;
• Project ammunition: The practice section's Portfolio Projects and Building a Deep Learning Project from Scratch provide real, valuable project experience for your resume;
• Paper bonus: The papers section's Classic Paper Deep Dives and Paper Map help you demonstrate depth in research-role interviews;
• Direction frontier: Large Language Models (LLMs) and Multimodal Models map to the current hottest role directions.

We recommend at least skimming the core pages of concepts and case-studies before starting the career process, otherwise your study plan will be so large it's impossible to execute.

Further Reading

• JD List: Open Roles at Major Domestic and Overseas Companies — Real JD breakdowns and skill keyword frequency statistics
• JD Knowledge Map — Turn JD skill keywords into a personal study plan
• Capability Match: What to Highlight in Your Resume — Four-part project writing and rewriting examples
• Interview Question Bank — Seven categories of interview questions and answer key points
• Portfolio Projects — Where the most valuable resume projects come from
• Learning Paths: Three Routes — Work backward from your target role to identify knowledge gaps

References

• Glassdoor. Machine Learning Engineer Salaries (continuously updated)
• Zhaopin. Algorithm Engineer Salary Survey (2026 data, verify before citing)
• Levels.fyi. Software Engineer Compensation (overseas role salaries, continuously updated)