Role roadmaps / ML Engineer Role flowchart
Train, evaluate, and serve models as systems: features, latency, and rollback. Closer to production than research.
Also called: Applied ML Engineer, ML Platform Engineer (IC)
You ship: A served model with a metric, a rollback, and a feature table that matches training.
After you can train a model locally — then the job starts
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:Data Scientist , MLOps Engineer , AI Engineer
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Start · ML Engineer Foundations Python + tensors NumPy first. PyTorch when the model is real. Know the memory. Feature SQL Point-in-time correct joins. Training-serving skew is a join bug. Metrics that match the product Precision/recall, calibration, latency. Accuracy is rarely the SLA. Daily craft Training loops you can replay Seeds, data versions, and a config that is not a screenshot. Serving paths Batch vs online. Warehouse is the wrong online store. Training-serving skew Same transform, two places, one test. Log both. How you ship Model CI Unit tests on transforms, eval on a slice, block on metric drop. Watch the live model Drift, latency, and a kill switch. Dashboards are not monitoring. Career moves MLE interview Coding, ML system design, and “how do you know it got worse?” Next role MLOps if you like platforms. Scientist if you like research. AI engineer if you like RAG. Keep practicing Study this node
Python + tensors NumPy first. PyTorch when the model is real. Know the memory.
NumPy first. PyTorch when the model is real. Know the memory.
NumPy first. PyTorch when the model is real. Know the memory.
Time to learn NumPy first. PyTorch when the model is real.
Prerequisites Memory of a batch log-softmax vs softmax then log What you need Python tensors track Log-softmax lab Concepts to study Tensors vs ndarrays Autograd at a conceptual level Batch dimensions and GPU memory Determinism: seeds are necessary not sufficient Tech stacks NumPy — AlwaysPyTorch — Default net stackJAX (some shops) — AltHow to study Python tensors track: softmax, log-softmax, cosine, top-k. What to produce A training step you can explain without a screenshot A memory budget for one batch Practice on this site Log-softmax lab; Python tensors track Pitfalls Starting in PyTorch without being able to write softmax Interview prompts Why log-softmax instead of softmax then log? Links
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