03 — North-Star Artifacts (The Eight Rungs)

Every hour you spend must eventually land in one of these eight rungs. If it doesn’t, it’s leisure or tutorial-hopping. That’s fine — just call it what it is. This file is the map from month → rung → public artifact → hiring signal.

Detail per rung lives in ../12_portfolio/. This file is the strategic overview.

The Ladder At A Glance

The two orange rungs (R5, R6) are the M9 hard gate. They convert everything before into a hire-able profile. Everything after them compounds; nothing before them fully counts.

Each Rung, Strategically

R1 · CPU Tiled Matmul Writeup · M2

  • What: A blog post walking through naive → tiled → blocked CPU matmul with measured cache effects on your own machine.

  • Published: Your blog (Medium / hashnode / hand-rolled), cross-posted to r/LocalLLaMA or r/programming.

  • Signal to hiring managers: This person actually measures things. Rare among ML engineers.

  • Detail: ../12_portfolio/01_rung_1_matmul_writeup.md

  • Zoho angle: None yet. Learn quietly.

R2 · SGEMM Ladder Repo · M4–M5

  • What: A public GitHub repo with 6–8 kernel versions in progression (naive → coalesced → tiled → register-tiled → vectorized → double-buffered → wmma tensor-core), each with an Nsight Compute screenshot in the README.

  • Published: GitHub, pinned on your profile.

  • Signal: This person can write CUDA. And more importantly, this person can explain why one version is faster than another with measurement, not folklore.

  • Detail: ../12_portfolio/02_rung_2_sgemm_ladder.md

  • Zoho angle: None yet.

R3 · Triton FlashAttention-2 · M6–M7

  • What: A Triton FA2 forward implementation matching SDPA numerics across shapes, with a benchmark sweep and honest gap analysis vs the official flash-attn package.

  • Published: GitHub + companion blog post explaining what you learned.

  • Signal: This person understands the algorithm that runs the world, at implementation depth. Rare enough that this alone gets you initial screens.

  • Detail: ../12_portfolio/03_rung_3_triton_fa2.md

  • Zoho angle: None yet. First mention is at R4.

R4 · Quantization Bake-Off Post · M8

  • What: One 8B model, five formats (GPTQ / AWQ / FP8 / GGUF Q4_K_M / bf16 baseline), speed table (batch 1 and batch 32) + quality table (perplexity + 3 tasks + KL-to-fp16). Uses llm-compressor (AutoGPTQ is deprecated).

  • Published: Blog + r/LocalLLaMA post. This community respects this content type deeply.

  • Signal: This person can prescribe quantization for a deployment scenario with real numbers to back the recommendation. This is a direct study signal.

  • Detail: ../12_portfolio/04_rung_4_quant_bakeoff.md

  • Zoho angle: One mention — the Sapphire Rapids DDR5 8-channel SMB deployment story from ../06_quantization/10_gguf_k_quants.md. Applied ML at Zoho meets on-prem quant.

R5 · Mini Inference Engine (MVP) · M9 · HARD GATE

  • What: Python + PyTorch + your Triton kernels. Async HTTP server, OpenAI-compatible /v1/chat/completions streaming endpoint, continuous-batching scheduler, paged KV cache with block manager, prefix reuse, FlashAttention adapted for paged KV (or FlashInfer). Benchmark harness producing latency-throughput curves vs vLLM. Target: within 3× vLLM at 8 req/s ShareGPT load.

  • Published: GitHub with detailed README, benchmark data committed, blog post explaining architectural decisions and where the gap to vLLM comes from.

  • Signal: This person understands inference engines at architectural depth, not user-of-vLLM depth. This is the artifact that changes the conversation.

  • Detail: ../12_portfolio/05_rung_5_mini_engine.md

  • Zoho angle: One mention — prefix-cache hit rate on Zoho agent traffic (sanitized: “75–94% on turns 2–4”). This is the data point that makes the engine’s design decisions feel earned.

R6 · First Merged PR · M9–M10 · HARD GATE

  • What: A merged (not just opened) pull request to one of: vLLM, SGLang, FlashInfer, llama.cpp. Small is fine — a docs fix, an example, a bug fix, a benchmark. Merged, not opened.

  • Published: GitHub PR link. Add to LinkedIn.

  • Signal: This person can navigate an unfamiliar 100K+ LOC codebase and land a change through maintainer review. The single strongest hiring signal in this field.

  • Detail: ../12_portfolio/06_rung_6_first_pr.md

  • Overlap warning: Median outside-contributor vLLM PR merge is ~14 days; first PRs skew longer. To hit R6 by end of M9, you must have the first draft PR open by mid-M8. This overlap is why R5 and R6 are sequenced with R6 opening while R5 is finishing.

R7 · Enterprise Reference Architecture · M11–M12

  • What: A design document. “Enterprise on-prem LLM platform on 8×H100 serving a 70B chat model + an 8B utility model, with prefix-cache-aware routing, autoscaled replicas, full observability, agentic workload profile.” Includes: hardware bill, model+quant+engine choices, sizing math, SLO commitments, observability plan, cost model in $/1M-token, agentic-workload load-test data.

  • Published: Blog / gist / GitHub repo with full YAML and drills. Cross-post to your target companies’ engineering communities.

  • Signal: This person can lead a production LLM platform. This is the staff-engineer signal.

  • Detail: ../12_portfolio/07_rung_7_reference_arch.md

  • Zoho angle: Built ON Zoho workload data. The 75–90% prefix hit-rate on real agentic traffic is the entire architectural justification for prefix-cache-aware routing. This artifact reads as practitioner-authored, not student-authored.

R8 · Sustained Contribution Area · M12–M13+

  • What: 6-month sustained pattern of contributions to a single project in a single area. Design doc → 3+ merged PRs → maybe a talk → maybe a CODEOWNERS mention. Not more PRs; deeper PRs in one area.

  • Published: The GitHub history is the artifact. Pinned to your LinkedIn.

  • Signal: This person owns something. Hire-able at staff-eng level. This is what converts loops to offers at rate.

  • Detail: ../12_portfolio/08_rung_8_sustained_contribution.md

  • Zoho angle: Makes the stay-vs-leave question explicit. If Zoho matches / offers infra role, this artifact travels with you either way.

  • Hard precondition: The Monday-morning ritual. Open the target project’s issue tracker filtered to your area, pick one thing to work on that week, sustain 6 months. If you cannot commit to that ritual, do not start R8. Sustain the rhythm or don’t start.

The Zoho-Specificity Gradient (This Is Intentional)

Rung

Zoho Mentions

Reads As

R1

0

Student learning fundamentals

R2

0

Student learning fundamentals

R3

0

Student learning fundamentals

R4

1 (SMB / on-prem)

Applied ML person exploring inference

R5

1 (agentic prefix hit rate)

Inference practitioner with a workload

R6

0

Contributor to a major project

R7

Load-bearing (workload data is the justification)

Staff-eng authoring for a production audience

R8

Explicit stay-vs-leave decision

Practitioner who owns an area

Read from top to bottom, the ladder is the story of a person becoming. Not a hobbyist who read books; a professional who converted their day job into a portfolio. Preserve this gradient. Do not front-load Zoho content into R1–R3 (looks like humblebrag) and do not withhold it in R7 (loses the differentiator).

The Publication Cadence

One artifact per ~6 weeks. Not more, not less:

M2  R1  ────┐
M5  R2  ────┤
M7  R3  ────┼── ~6-week cadence
M8  R4  ────┤
M9  R5+R6  ─┘
M11 R7  ─── (2-month build)
M13 R8  ─── (ongoing rhythm)

This cadence matters. Shipping too fast loses depth. Shipping too slow loses momentum. Six weeks feels right in practice — enough to do the thing, write it up, take a breath.

What Each Rung Is Worth (Hiring-Signal Weight)

Rough calibration from vLLM/SGLang/Anyscale/Baseten hire patterns:

Rung

Weight (0–10)

Notes

R1

2

Baseline signal. Everyone has one at some level.

R2

4

Distinguishes from most applied ML engineers.

R3

6

Very few people have this. Cracks open initial screens.

R4

5

Practitioner signal. Combined with R3 becomes strong.

R5

8

The single biggest solo artifact.

R6

9

The most portable signal. Merged PR travels everywhere.

R7

8

Staff-eng signal. Combined with R5+R6 is offer-conversion tier.

R8

10

Ownership. This is what senior/staff hires want to see.

R1–R4 make you interviewable. R5+R6 make you an offer candidate. R7+R8 make you a staff-level hire.

The One Sentence

The ladder is the CV. Every rung is public. Every rung compounds. Miss a rung and you can still finish; miss R5+R6 and the year didn’t count.

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