Datatech Analytics
Machine Learning Engineer
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Global Financial Services Institution | Mid–Senior | Location London
Make ML Models Faster
Building a model is one thing. Making it faster, leaner, and reliable in production is where the engineering gets interesting. Our client is looking for a Machine Learning Engineer focused on inference optimisation and performance. You’ll work across risk, payments, and client products, where low latency and efficiency genuinely matter. The work is predominantly CPU-based, low batch, and latency sensitive, with models running in a regulated environment.
GPU Background?
Absolutely still relevant. If you understand why your CUDA/GPU optimisation improved performance, the CPU inference toolchain can be learned.
What You’ll Do
- Take production models and find ways to make them perform better
- Profile models and identify bottlenecks
- Reduce inference latency and improve throughput
- Apply quantisation while protecting model accuracy
- Optimise computation graphs and threading
- Benchmark changes against a clear baseline
- Deploy and monitor optimised models
- Identify the next performance improvement
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We want engineers who can explain the numbers:
- What was the problem?
- What did you change?
- What difference did it make?
What You’ll Need
- Strong Python plus C++, Rust, Go, or Java
- Production experience optimising ML inference
- Practical experience with quantisation
- Experience with at least two of: ONNX Runtime, OpenVINO, oneDNN, IPEX, TVM, TensorRT, vLLM, or llama.cpp
- PyTorch or TensorFlow
- XGBoost or LightGBM
- Docker, Kubernetes, and CI/CD
- A strong performance-engineering mindset
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Useful Experience
- CPU optimisation: AVX-512, VNNI, AMX, or NUMA
- Real-time or streaming inference
- Kernel-level optimisation
- Financial services
- Open-source ML tooling
Senior Level
For Senior Engineers, we want a specific example of an optimisation you led.
- What was the baseline?
- What did you change?
- What was the measurable result?
What This Isn't
This isn't a research role, distributed GPU training role, or pure ML platform position. The focus is simple: make production ML models faster, more efficient, and more reliable. You’ll also work within a regulated environment, so experience with model documentation, validation, and monitoring is important.
Confidential enquiries: justin.toomey@datatech.org.uk
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