How to Build a Machine Learning Portfolio That Gets Interviews

How do you build an ML portfolio?

To build an ML portfolio that gets interviews, publish two to four finished projects matched to the role you want. Pair one applied project on real data with one depth project that contains a component you built from scratch and verified. Give each project a baseline, measured results, a clear README and one command to reproduce it.

How many projects should a machine learning portfolio have?

Two to four complete projects is enough for most ML portfolios. Reviewers tend to look closely at one or two of them, so the depth of each project matters more than the count.

A strong default is one applied project and one depth project. The applied project shows that you can take a messy problem from raw data to a measured result, and the depth project shows that you understand the methods underneath, through a model implemented from scratch, a reproduced paper or a benchmarked GPU kernel.

Beginners and career switchers should build the depth project first, because its fundamentals make the applied project stronger and prepare you for implementation questions. If you already work in ML, lead with the applied project and add a depth project aimed at the role you want next.

Why do most ML portfolios get ignored?

Most ML portfolios get ignored because they look like every other applicant's and leave a reviewer's basic questions unanswered. Titanic survival, MNIST digits and iris classification show that you followed a tutorial, as many other applicants did. A notebook with no README asks the reviewer to run code to find out what it does, and few will.

A score such as 0.82 AUC means little without a baseline beside it, and a project that is mostly calls to model.fit() hides whether you understand the model. Ten unfinished repositories also look weaker than two complete ones.

Which ML portfolio projects should you build for your target role?

Build the pair of projects that gives your target role the evidence it looks for, such as statistical judgment for a data scientist or benchmarked kernels for a GPU engineer. Pick a role on the wall below to see its pair.

The table lists all twelve project ideas from the wall with their level and the roles they suit best.

Foundations and applied projects

A from-scratch ML library implements linear and logistic regression, k-means, a decision tree and the common metrics in NumPy, each tested against a reference library. The Cracking ML plan works through these models, including Logistic Regression from Scratch. An evaluation deep dive compares accuracy, precision and recall, ROC AUC, PR AUC and calibration on one problem and recommends one metric for a stated decision, and AUC-ROC from Scratch and the metrics and evaluation problems build those pieces. A loss function study trains one model with binary cross-entropy and with mean squared error and compares their gradients.

The end-to-end applied project uses a real, messy dataset for a specific problem, with cleaning, leakage-safe validation, a baseline, error analysis and a small demo. Senior candidates can add a system design write-up for a recommender or a fraud detector, and the ML System Design lessons cover the data, serving and monitoring sides of that document.

LLM, research and GPU projects

A GPT-style model built in PyTorch without nn.Transformer becomes convincing when it loads the released GPT-2 small weights and matches a reference implementation's outputs. The Build LLM from Scratch plan and the guided tiny GPT project cover that build. An inference study measures how a KV cache, batching and quantization change latency, memory and quality, which the Inference Engineering plan teaches. In an LLM app, the evaluation harness with a fixed test set matters most, and the RAG from scratch project is a good start.

A paper reproduction should reproduce the paper's finding, list every difference in setup, and add ablations that test which components matter. The Research section breaks well-known papers into tested coding steps. A training dynamics study changes only the initialization, the normalization or the optimizer and plots loss and gradient norms, and Implement Adam Optimizer Step is a useful warm-up for it.

A CUDA kernel suite needs a naive and an optimized version of each kernel, such as softmax and tiled matrix multiplication, with timings against a library baseline across several input sizes. The CUDA Kernels plan builds these, and for the second project you can write the same fused softmax in the Triton Kernels plan and compare code length and speed.

What makes a machine learning project credible to a reviewer?

A machine learning project is credible when it solves a specific problem, compares results with a baseline, reports a metric that fits the problem, and includes a component you built and verified yourself. It also says what did not work and gives one command that reproduces the result. Step through the commits below to watch a tutorial project gain each of these.

Put the result, the baseline, the method and one figure on the first screen of the README, because reviewers decide quickly whether to keep reading. This template shows that first screen and the sections after it.

# [Specific problem]: [approach in a few words]

Result:    [metric] of [value] vs [baseline value] for [baseline], on [data and split]
Method:    [model], with [component built from scratch] verified against [reference]
Reproduce: python train.py --seed 0, about [runtime] on [hardware]

![Bar chart of validation [metric] for the baseline and each variant](docs/results.png)

## Problem
## Data
## Approach
## Results
## Decisions and trade-offs
## What did not work
## Limitations
## Reproduce

Give each figure alt text that says what it shows. For a model other people might use, add a short model card with its intended use, evaluation data and known limits, following the Model Cards for Model Reporting paper by Mitchell and colleagues.

Should portfolio projects include code you built from scratch?

Yes, at least one meaningful component in each depth project should be your own code, verified against a reference implementation. A training loop, a metric, an attention layer or a GPU kernel that matches a library on tests is strong evidence that you understand the method, and libraries can handle everything else. The essay on learning machine learning from scratch makes the longer case for this.

Candidates commonly report being asked to build the hardest-sounding part of their own project live, and a component you have already written and tested is one you can rebuild under pressure. A solved practice problem becomes a portfolio piece in four steps.

1. Solve the problems behind your project on TensorTonic until each one passes its tests and you can explain it without notes.
2. Rewrite the implementation from a blank file in your own repository, with your own structure and tests.
3. Extend it by applying it to real data, adding ablations, benchmarking more inputs or combining components into a working system.
4. Write it up with the README template above.

For example, Tiled Matrix Multiplication as a CUDA Kernel teaches shared-memory tiling and shows your kernel's timings across several GPU models. The portfolio version is your own kernel suite with a README that explains why the speedup changes from one GPU to another. The guided projects follow the same path for larger builds such as an inference engine.

How do you build an ML portfolio on GitHub?

Build it by pinning your two to four best repositories, giving each one a specific name and a one-line description, and making sure each pinned repository opens with a README that shows the result on the first screen. A name like sarcasm-detection-reviews tells a reviewer far more than nlp-project-2.

Keep reusable code in modules and save notebooks for exploration and figures. Add a requirements file and a license, and link trained weights or logs for anything expensive to rerun. A short profile README that names the role you want also helps.

How do you turn ML projects into resume bullets?

Turn each project into one or two bullets that say what you built, how you verified it and the measured result against a baseline, and leave out any number you cannot defend in an interview. Replace the placeholders in square brackets with your own measured results.

Any bullet on your resume can turn into an interview question. The guide on explaining an ML project in an interview covers how to walk through each one. The timed mock interviews, such as the ML Engineer Mock Interview, let you rehearse the implementation side against a clock. If a take-home is part of the process, the ML take-home guide uses the same README habits.

What else do people ask about ML portfolios?

Which problems should you practice before you build?

Each of these problems is the verified core of one project from this guide. Then browse the problem library for the rest of your build.

• Logistic Regression Training Loop is the training core of a from-scratch ML library.
• Compute AUC (Area Under ROC) is the metric at the center of an evaluation deep dive.
• Implement Causal Masking for Attention is the mask a GPT-style model must get right before it can match released weights.
• Tiled Matrix Multiplication as a CUDA Kernel is the main kernel in a benchmark suite.

Related guides

• How to explain an ML project in an interview
• ML take-home assignments: scope, structure and what reviewers look for
• Learning machine learning from scratch
• How to prepare for an ML engineer interview
• ML engineer vs data scientist