Junior+ ML Engineer (Model Architecture, Training and Edge Optimisation)

Що робити

Design and implement new model architectures in PyTorch, both transformer-based and beyond
Train models, sometimes from scratch, and run experiments to compare architectures
Build training and evaluation pipelines on screen-understanding and computer-control data
Profile latency, memory use and KV-cache behaviour on long, high-resolution screen inputs
Compress models for edge chips using quantisation (INT8/INT4), pruning and distillation

Deep architectural understanding of Transformers and Attention: you know the mathematical and operational differences between MHA, MQA, GQA, FlashAttention, and linear attention variants; you understand KV-cache scaling and memory access bottlenecks.
Low-level PyTorch fluency: writing custom PyTorch modules, implementing non-standard forward/backward logic, vectorised tensor operations, and profiling memory/compute bottlenecks using PyTorch Profiler / Nsight.
Experience with model training & convergence: stability tricks, learning rate schedules, gradient clipping, mixed-precision training (BF16/FP16), and diagnosing loss spikes or exploding/vanishing gradients.
Rigorous mathematical foundation: comfortable with high-dimensional linear algebra, matrix calculus, multivariable optimization, and probability theory.
Practical understanding of model compression: Post-Training Quantization (PTQ) vs Quantization-Aware Training (QAT), outlier activations, weight-only vs weight-and-activation quantization (INT8, INT4, FP8).

Experience with vision transformers or vision-language models
Interest in non-transformer architectures (state-space models, linear attention, recurrent designs)
Experience exporting to ONNX, TensorRT, OpenVINO or NPU toolchains
Basic C++, enough to read inference code
Degree in Applied Mathematics, Computer Science or Physics