ML Engineer Resume
Skills & ATS Keywords

Aim for roughly 35 to 50 named tools, frameworks, and patterns, sorted into 8 short category rows (languages, distributed training, serving, MLOps, feature store, cloud, monitoring, orchestration). Drop below 30 and the page reads junior for an MLE; clear 55 and it starts looking inflated. Treat the Skills block as a contract: each entry should be defendable by at least one production-ML bullet that names the model, the runtime, or the pipeline you ran it through. If the bullet is not there, the line is dead weight.

Drop the block immediately beneath your Profile Summary, before the Work Experience list. Hiring platforms scan from the top, and most lift terms more dependably when they sit inside a tagged section close to the top of the file. For an MLE, structure it as 8 grouped rows (training frameworks, distributed training, serving, MLOps, feature store, cloud, drift monitoring, orchestration) so the parser reads tidy clusters instead of a single sentence-long string of commas.

Copy the JD into a notes file, highlight every framework, service name, and noun that surfaces twice or more, and collapse those into a 12 to 18 item shortlist. Run that shortlist against your Skills rows and your bullets. Anything repeating in the posting that you genuinely use but that is missing from the page goes into the right row, plus the bullet where you actually trained, served, or monitored the model. Push the revised file through an ATS Checker to confirm the parser surfaces the tokens you expect.

Both list Python and PyTorch, but the spine of the resume is different. A Data Scientist leads with science: experimentation, A/B test design, causal inference, statistical rigor, and notebook-to- stakeholder communication. An ML Engineer leads with production: training pipelines, model serving, GPU autoscaling, p99 inference latency, drift monitoring, and the runtime stack (Triton, vLLM, TensorRT, SageMaker, Vertex AI). If your bullets sound like lift, p-value, and CUPED, you are pitching DS; if they sound like 18k QPS, p99 under 35ms, and 38% throughput uplift on TensorRT-LLM, you are pitching MLE. Pick one target and order the bullets so the matching nouns surface in the first scan.

List what you have actually shipped, not a paired alias. If your real exposure is single-node multi-GPU DDP on PyTorch Lightning, write that line as DDP only and skip FSDP. Recruiters who screen MLE resumes for senior FAANG and frontier-model roles check this in the loop, and a fabricated FSDP claim usually unwinds during the architecture question. Once you have run an FSDP migration end-to-end, including the sharding strategy, mixed precision, and gradient checkpointing decisions, both belong on the row.

It depends on the lane. For application MLE roles (recommendations, fraud, ranking), the GPU primitives are nice-to-have and one cluster row covering Kubernetes plus NVIDIA GPU operator is usually enough. For inference-platform roles, foundation-model teams, or anything at NVIDIA, Anthropic, or OpenAI, the GPU stack is the resume: CUDA basics, NCCL, Triton Inference Server, TensorRT-LLM, vLLM, paged attention, and continuous batching all earn their place. Map the depth on your page to the lane you are actually targeting.

Four numbers carry most of the weight on an MLE resume: latency (p50, p95, p99 inference time, online vs offline), throughput (QPS served, tokens per second, models per fleet), cost (cost-per-inference, GPU hours, training credits saved through quantization or batching), and reliability or quality (drift incidents prevented, eval regressions caught, training-eval skew incidents per quarter). A bullet that names the model, the runtime stack, the QPS, the p99, and the dollar impact reads as a senior MLE shipping production work. Phrases like improved performance or optimized inference get parsed once and skipped on the human read.