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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in deploying and optimizing machine learning models within real-world constraints, utilizing techniques such as quantization and pruning. Proficient in Python and C/C++ for model development and low-level optimization, with a strong focus on collaboration and ownership throughout the deployment process.
Highest-signal resume keywords
Model Optimization TechniquesPython ProgrammingC/C++ Low-Level OptimizationExperience With GPUs, NPUs, TPUsModel Deployment In Production
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
QuantizationPruningKnowledge DistillationModel ProfilingModel CompressionDomain AdaptationCUDATritonHIP/ROCmModel Serving
Soft Skills
CollaborationOwnershipAdaptability
Tools & Technologies
QuTwo OSOpen-Weight Model FamiliesRetrieval PipelinesStructured PromptingAgent Loops
Industry Keywords
Machine LearningApplied ResearchModel InferenceEdge MLSynthetic Data Generation
Tech Stack
Tools & technologiesPythonSwitchingC++
About the role
Key responsibilities & impact- Take trained models from researchers and clients and make them run within real memory, latency and hardware limits
- Deploy models in client environments and in QuTwo OS
- Work closely with researchers, engineers and client teams
- Bring research into production
- Apply model compression techniques and optimise models for specific hardware
Requirements
What you’ll need- Applied quantisation, pruning and knowledge distillation to real models, with ability to recover accuracy through fine-tuning, retraining or other healing methods
- Ability to profile model memory, bandwidth and compute usage and translate findings into optimisation decisions
- Experience optimising models for accelerators such as GPUs, NPUs or TPUs
- Fluency with current open-weight model families and related tooling
- Experience building domain adaptation harnesses, including retrieval pipelines, tool integrations, structured prompting or agent loops
- Python experience with research code and model artefacts
- C/C++ experience for low-level optimisation
- Several years of hands-on experience shipping models to production in industry or an applied research lab
- Comfort switching between projects and hardware stacks and taking ownership from first idea through final deployment
- Experience with custom kernel development using CUDA, Triton, HIP/ROCm or Metal is a plus, not a requirement
- Experience with on-device and edge ML is a plus, not a requirement
- Experience optimising concurrency, parallel execution and memory bandwidth for efficient inference is a plus, not a requirement
- Experience with model serving and deployment at scale is a plus, not a requirement
- Experience generating synthetic data to fine-tune or heal compressed models is a plus, not a requirement