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# MLOps Engineer - AI Systems

- **Company:** Mercor
- **Location:** Berlin, Germany (Remote available)
- **Experience:** Experienced
- **Salary:** €187,200.0 - €249,600.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Profiling, Nvidia CUDA, Shard (Database Architecture), Software Debugging, Distributed Computing Environment, Distributed Systems, Windows Software Trace Preprocessor, Pytorch, Large Language Models, AWS Trainium, Low Latency, SGLang, Machine Learning Operations, TensorRT, VLLM, CUTLASS, Megatron, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 25, 2026
- **Apply:** https://www.adzuna.de/details/5940356155

## About the Role

Must-Have

 * 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator performance engineering.
 * Practical experience in writing or optimizing custom kernels (Pallas, Triton, CUDA, CUTLASS), performance profiling and trace analysis (JAX/XLA profiler, Kineto, torch.profiler, Nsight), debugging distributed or accelerator-bound workloads, serving large language models at scale (vLLM, SGLang, TensorRT-LLM, JetStream, KV cache, paged attention, continuous batching).
 * Working production experience with JAX and/or PyTorch. Framework-level depth is a strong plus: custom operators, sharding and distributed training (pjit/shard_map, FSDP, Megatron, MaxText), or compiler and graph-level work (XLA/StableHLO, torch.compile, MLIR).
 * Experience with modern accelerators such as TPU (v4 to v6e), H100, B200, Trainium or MI300, and the ability to reason about throughput, latency, and memory trade-offs.
 * Demonstrable career progression.
 * Ability to engage reliably for at least 40 hours/week during weekdays.
 * Strong written communication skills and the ability to explain complex technical decisions clearly.

## Description

* Design challenging tasks across kernels, profiling, debugging, and inference serving. Write accurate, well-structured solutions to expose where frontier models fail on accelerator code.
 * Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, compilers, and training infrastructure topics.
 * Evaluate ML systems tasks and solutions. Provide clear written technical feedback that stands up to expert review.
 * Develop guidelines and detailed rubrics covering kernel optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs.
 * Collaborate with other subject matter experts to keep training data consistent and accurate.