Internship: Data Engineering, AI Infrastructure & Systems Optimization
Location: Casablanca, Morocco
Company: Cognitive Machines Technologies (CMX)
Period: February to June 2027
Duration: 4 months | Final-Year Internship (PFE)
Behind every intelligent system, there's an infrastructure that must process data, execute computations, handle failures, and deliver results efficiently.
At CMX, we're interested in engineering that infrastructure, from data pipelines to high-performance AI inference systems.
We're looking for students who enjoy understanding how things work under the hood, measuring performance, and finding better ways to build systems.
Research Areas
- * Data engineering, ETL/ELT, streaming, and event-driven architectures.
- Data platforms, governance, metadata, lineage, and data quality.
- Vector databases, knowledge graphs, and distributed storage.
- AI inference, vLLM, ONNX Runtime, and model serving.
- Modern C++, Python, CUDA, SIMD, and parallel computing.
- CPU/GPU optimization, concurrency, memory management, and profiling.
- Kubernetes, cloud-native infrastructure, GitOps, and distributed systems.
- Observability, reliability, FinOps, and energy-efficient computing.
Your Research MissionUsing our CMX Cognitive Framework, you'll investigate existing technologies, model system constraints, design an architecture, implement selected components, and benchmark your results.
We care about measurable engineering: latency, throughput, reliability, resource utilization, and cost.
Three Questions Before You Apply
- You double the number of GPUs serving an AI model, but the number of requests processed per second barely increases. What are three possible explanations? How would you investigate them?
- Two enterprise applications contain different addresses for the same customer. Both claim their information is correct. How would you determine which value should be trusted without losing the history of changes?
- An AI inference system costs 100,000 MAD per month to operate. Management asks you to reduce the cost by 50% without degrading service quality. What would you measure first? Which optimizations would you investigate? How would you prove that quality has been preserved?
How to ApplyPlease send the following to [email protected]:
- Your CV.
- A Word document (.docx), maximum two pages, containing your answers to all three questions.
- Your GitHub profile, portfolio, or relevant projects, if available.
You may use AI tools and external resources, but you must explain your own reasoning and disclose the tools or sources used.
Important: Applications without answers to all three questions in the requested Word format will not proceed to the next selection stage.
Shortlisted candidates will be invited to the CMX Talent Acquisition Day in early January 2027 for an in-person assessment and interview.
You don't need to be an expert in every technology listed here.
What matters is your curiosity, your engineering discipline, and your willingness to measure rather than assume.
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