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More fit checks, tailored CVs, and complete prep for every role.
Job Description
What you'll do
Read research papers and source code critically. Reconstruct the mechanism, check its assumptions and complexity, and turn uncertain claims into testable hypotheses.
Implement and modify model components, training loops and inference paths in Python and PyTorch. Build reference implementations and verify mathematical and numerical correctness before optimizing.
Own experiments end to end: prepare data and environments, launch and monitor GPU runs, recover failed jobs, and preserve configurations, checkpoints and results so others can reproduce them.
Design matched baselines and ablations. Test quality and failure modes across model sizes, datasets and context lengths, including retrieval, reasoning and generalization.
Measure memory use, data movement, latency and throughput. Profile prefill and decoding separately, account for routing and kernel overhead, and check whether theoretical savings produce measurable system gains.
Diagnose failures across mathematics, architecture, data and implementation. Work with researchers and compiler, runtime and hardware engineers to improve designs and integrate validated components.
Requirements
3 to 5 years of hands-on AI/ML engineering or applied research experience, with evidence of
implementing, running and stress-testing model work under real compute constraints.
Strong foundations in linear algebra, probability, optimization and numerical methods. You can
work through equations, follow tensor shapes and gradients, and connect a mathematical
formulation to its implementation.
Deep understanding of Transformer internals, attention and autoregressive inference, plus
familiarity with recurrent, state-space or hybrid sequence models. You can reason about how a
model stores, updates and retrieves information.
Practical depth in at least two area of efficient AI, such as memory-efficient language models and
long-context inference, including KV-cache compression, sparse or linear attention, model
memory and compression, quantization, conditional computation, or inference optimization, and
the ability to learn adjacent approaches deeply.
Strong Python and PyTorch skills, including custom modules, training and inference code, testing
and debugging. You can understand and repair code written by others or generated with AI
assistance.
Experience running GPU experiments in Linux environments, using version control, profiling
workloads and managing reproducible results. You understand memory capacity, bandwidth,
parallelism and numerical precision.
Sound experimental judgment. You can distinguish a promising small-scale result from evidence
that a method generalizes, identify confounded comparisons, and explain negative results and
remaining uncertainty.
The ability to work from an incomplete specification, choose the next useful experiment and
communicate findings clearly to both researchers and engineers
Additional experience we value
C++, CUDA or Triton; custom GPU kernels; distributed training; or work with compilers, inference runtimes and hardware-aware algorithm design.
Model distillation, architecture conversion, fine-tuning or pretraining; scaling prototypes to larger models; or research implementations and open-source contributions that others have used.