Interactive page (recommended): DeepSeek招聘职位技能分析.html
Source: talent.deepseek.com · site snapshot 2026-09-07T06:09:32Z · 34 open roles in total
JD body text is kept as in the official HTML. Skill tags are extracted from the body by rules, for filtering and counts; they are not official tags. Apply via the official site / Moka.
The official site groups roles as “full-stack development / algorithms, AI core-systems R&D, operations, product, model-data strategy, deep-learning researcher, functions.” That cut is useful for applying, but too coarse for “what should I practice”—“full-stack development / algorithms” simultaneously holds front-end, server-side, Agent Harness, pre-training data, and testing. This page recuts by work content into 10 tracks, and counts in how many roles a skill appears (each role counted at most once; required = appears before “plus / nice-to-have”).
How to use the interactive page
White-background reader: click a role name on the left; read the full JD on the right.
- Category / location is a Chip row, selected by horizontal scroll; skills stack via a dropdown. The current filter shows as removable tags.
- Each JD shows at most 6 skill Chips above the body; the rest sit under “more skills.” Clicking a Chip filters the list by that skill.
- Multi-track roles (for example server-side) use direction Tabs; only one direction is expanded at a time.
- “Highlight skills” is off by default, so the original text stays clean; when on, skill terms get a blue wash (no 【】, and cultural words such as open source are not marked). The switch is written into the address bar as
?hl=1. - The top-bar skill notes are 5 short cards + the first 8 skill bars. Clicking a bar or a category returns to the role list with that filter applied.
- Arrow keys or
j/kmove up and down in the list. On a phone, tap the list first, then “← role list” to go back.
Track mapping (headcount)
| Track | Roles | Rough official-site home |
|---|---|---|
| Functional support | 7 | HR / legal / finance / procurement / admin / management trainee / cross-domain |
| Data engineering / data strategy | 6 | Pre-training data + model-data strategy |
| Deep-learning research | 5 | Researchers + AI search |
| Testing / operations / infrastructure | 4 | Operations + test development |
| LLM training / inference | 3 | Framework / operator compilation / deep-learning R&D |
| Supercomputing / storage / clusters | 3 | AI core-systems R&D |
| Agent engineering | 2 | Full-stack development / algorithms |
| Product / operations | 2 | Product department |
| Front-end / client | 1 | Full-stack development / algorithms |
| Back-end / server-side | 1 | Full-stack development / algorithms (contains 6 directions) |
Locations counted by role (one role may list multiple cities): Beijing 28 · Hangzhou 30 · Ulanqab 2 (IDC, procurement).
Skill occurrence counts (every tag that hits)
| Rank | Skill | Roles | Required | Plus | Group |
|---|---|---|---|---|---|
| 1 | Cross-team collaboration | 20 | 17 | 3 | Collaboration |
| 2 | Open-source contribution | 15 | 6 | 9 | Evidence |
| 3 | Inference systems / KV Cache | 14 | 10 | 4 | Model training |
| 4 | Agent / Harness | 11 | 11 | 0 | Agent |
| 5 | Python | 11 | 9 | 2 | Language |
| 6 | Rust | 11 | 9 | 2 | Language |
| 7 | C++ | 10 | 9 | 1 | Language |
| 8 | Storage / filesystems | 10 | 9 | 1 | Systems engineering |
| 9 | Data pipelines / cleaning | 9 | 8 | 1 | Data |
| 10 | Evaluation / Benchmark | 9 | 8 | 1 | Model training |
| 11 | Linux / kernel / OS | 9 | 5 | 4 | Systems engineering |
| 12 | Distributed systems | 8 | 6 | 2 | Systems engineering |
| 13 | Papers / top conferences | 8 | 1 | 7 | Evidence |
| 14 | GPU / NPU / 10k-GPU clusters | 7 | 6 | 1 | Model training |
| 15 | High-performance networking / RDMA | 7 | 4 | 3 | Systems engineering |
| 16 | Contests (NOI/ICPC, etc.) | 7 | 0 | 7 | Evidence |
| 17 | Product sense / user insight | 6 | 6 | 0 | Product / org |
| 18 | Coding with agents | 6 | 5 | 1 | Agent |
| 19 | Post-training / RL | 6 | 4 | 2 | Model training |
| 20 | High concurrency / high availability | 6 | 3 | 3 | Systems engineering |
| 21 | PyTorch | 6 | 2 | 4 | Model training |
| 22 | Multimodality | 5 | 5 | 0 | Model training |
| 23 | Go / Golang | 5 | 4 | 1 | Language |
| 24 | Observability / monitoring | 5 | 4 | 1 | Systems engineering |
| 25 | Databases / caches / queues | 5 | 3 | 2 | Systems engineering |
The fuller set of 45 items is in the interactive page’s tag area. The conclusions below are a comparison reading, not official-site wording.
Comparison conclusions
1. This is not an internet-company org chart of “one front-end team + one back-end team.”
There is only 1 front-end role (web + two directions, iOS/Android). There is 1 server-side role, but it splits internally into six directions: research platform, Agent framework, R&D-efficiency infrastructure, API, online services, data engineering. The real headcount density is in data strategy, researchers, training / supercomputing systems.
2. Language stack: Python and Rust are tied (11 roles each), C++ 10 roles.
Systems roles almost always list Rust + C++ together, not as an either/or. Go appears in server-side / testing / operations development. JavaScript/TypeScript is a required skill only on the front-end role. If you want to match DeepSeek technical roles, the priority order is closer to Python + (Rust|C++) + Linux/distributed than to the front-end ecosystem.
3. Agent has moved from a product word to a hiring baseline.
11 roles mention Agent / Harness directly in the body, and all of those count as required—none appear only under plus. Server-side, Harness, product, design, and data product manager also require “you yourself are a heavy user” or the ability to write code with an agent. The Harness role even names Claude Code / Codex / Cursor / OpenCode / Manus / Hermes.
4. “LLM training” is a vertically sliced system, not a job title.
| Layer | Roles | More specific required skills |
|---|---|---|
| Data fuel | Pre-training data engineer, Code Agent data engineer, data product manager | Pipelines, evaluation, domain taste, RL environments |
| Algorithms | Pre-training / post-training / multimodal / Frontier researchers | Training dynamics, RLHF/GRPO, continual learning, vision encoders |
| Framework | LLM training / inference framework engineer, deep-learning R&D engineer | PyTorch, MoE, long context, asynchronous RL, KV Cache |
| Operators | High-performance operator / communication / compiler | CUDA / Ascend C / TileLang, NCCL, RDMA |
| Cluster and storage | Supercomputing, distributed storage, R&D infrastructure | Scheduling, RDMA, filesystems, cross-layer debugging |
| Treating the cluster as utilities | AI platform operations, IT, IDC | Linux, GPU-cluster stability, liquid cooling / power (IDC) |
5. Open source is a cultural signal.
15 roles mention open source; 9 of those place it under plus. The R&D infrastructure role states that they look at GitHub commit history, not star counts. Papers and NOI/ICPC are almost entirely plus (required almost never), which means they are plus leverage, not an entry ticket.
6. One JD is empty.
“AI product operations (experience and service)” has official-site body text of only “（新部门，虚位以待）” (“new department, position held open”). The interactive page keeps it as-is.
All roles (by track)
Front-end / client (1)
- Front-end / client development engineer · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time
Back-end / server-side (1)
- Server-side development engineer · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time
Agent engineering (2)
- Agent elastic-compute R&D engineer · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time
- Agent Harness team · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time (researcher / R&D / DevRel / product / design / project manager / operations)
LLM training / inference (3)
- Deep-learning R&D engineer · full-stack development / algorithms · Beijing · intern · full-time
- High-performance operator / communication / compiler engineer · AI core-systems R&D · Beijing / Hangzhou · intern · full-time
- LLM training / inference framework engineer · AI core-systems R&D · Beijing / Hangzhou · intern · full-time
Supercomputing / storage / clusters (3)
- Supercomputing-cluster R&D engineer · AI core-systems R&D · Beijing / Hangzhou · intern · full-time
- R&D infrastructure engineer · AI core-systems R&D · Beijing / Hangzhou
- High-performance distributed-storage engineer · AI core-systems R&D · Hangzhou · intern · full-time
Deep-learning research (5)
- AI search algorithm / architecture engineer · full-stack development / algorithms · Beijing · intern · full-time
- Frontier (continual learning / self-evolution / new paradigms) researcher · deep-learning researcher · Beijing · intern · full-time
- Pre-training (data / algorithms) researcher · deep-learning researcher · Beijing · intern · full-time
- Post-training (data / algorithms) researcher · deep-learning researcher · Beijing / Hangzhou · intern · full-time
- Multimodal understanding (data / algorithms) researcher · deep-learning researcher · Beijing / Hangzhou · intern · full-time
Data engineering / data strategy (6)
- Pre-training data engineer · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time
- Code Agent data engineer · model-data strategy · Beijing · intern · full-time (front-end / mobile / back-end / Web3D / games / security specialty)
- General-agent data product manager (office / daily life / search) · model-data strategy · Beijing / Hangzhou · intern · full-time
- Specialized-domain data product manager (low-resource languages, medicine, law, and other disciplines) · model-data strategy · Beijing / Hangzhou · intern · full-time
- AI creation data product manager · model-data strategy · Beijing / Hangzhou · intern · full-time
- Affective-intelligence data product manager · model-data strategy · Beijing / Hangzhou · intern · full-time
Testing / operations / infrastructure (4)
- Test-development engineer · full-stack development / algorithms · Hangzhou · intern · full-time
- AI-platform operations engineer · operations · Hangzhou · intern · full-time
- IT infrastructure team · operations · Beijing / Hangzhou · intern · full-time
- IDC datacenter team · operations · Beijing / Ulanqab / Hangzhou · full-time
Product / operations (2)
- AI product manager · product department · Beijing / Hangzhou · intern · full-time
- AI product operations (experience and service) · product department · Beijing / Hangzhou · JD original text almost empty
Functional support (7)
- AI cross-domain technical talent · full-stack development / algorithms · Beijing / Hangzhou · intern · full-time
- HR team · Beijing / Hangzhou · full-time
- AGI core-business management trainee · Beijing / Hangzhou · intern · full-time
- Legal team · Hangzhou · full-time
- Finance team · Hangzhou · full-time
- Procurement team · Ulanqab / Hangzhou · full-time
- Administration team · Beijing / Hangzhou · intern · full-time
Full responsibilities, requirements, and plus items are in the interactive page’s right-hand detail pane, or in each role’s Moka link.
Source data and regeneration
调查报告/deepseek-talent-jobs.json: original text of the 34 roles extracted from the official site’s JS bundle viaJSON.parsescripts/build-deepseek-talent-html.py: assigns tracks, extracts skills, generates the interactive HTML
python3 scripts/build-deepseek-talent-html.py