Senior AI Engineer
New
S
Sureel AIAI infrastructure
Listing location: Toronto, Ontario / Vancouver; Workplace type: RemoteFull-TimeSenior
SalaryBase salary of $110,000 - $145,000 CAD; eligible for a performance based annual bonus
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Job Details
- Required Skills
- PythonMachine LearningPyTorchMLOps
Requirements
- Have a strong scientific foundation in machine learning, AI, computer science, electrical engineering, mathematics, physics, or another quantitative field.
- Have an advanced degree or equivalent scientific depth developed through substantial research and engineering experience.
- Understand machine learning deeply enough to change algorithms, architectures, objectives, representations, training procedures, or evaluation methods as needed.
- Be able to reason from first principles, develop hypotheses, and use experiments to test them.
- Demonstrate technical originality through research, algorithms, patents, open-source contributions, unconventional engineering solutions, or products built without an established playbook.
- Be highly proficient in Python and modern ML frameworks such as PyTorch.
- Understand modern neural architectures, generative models, and multimodal systems.
- Understand experimental design, statistics, benchmarking, and failure analysis.
- Have strong software engineering skills and care about code quality, reproducibility, performance, and maintainability.
- Be able to collaborate and build production AI systems.
- Actively use AI tools in research, experimentation, and development while critically reviewing generated work and taking responsibility for shipped code and systems.
Responsibilities
- Take AI problems from initial hypothesis and algorithm design through validation, deployment, and iteration.
- Develop algorithms, representations, objectives, evaluation methods, and system designs when existing approaches fall short.
- Evaluate, challenge, and extend current research against the role's specific problems and goals.
- Design benchmarks, ablation studies, and statistical evaluations, comparing methods with academic and industry approaches.
- Work across training-data attribution, model influence, interpretability, embeddings, multimodal similarity, AI detection, guardrails, privacy-preserving observability, and large-scale retrieval.
- Build prototypes and turn successful approaches into scalable production systems.
- Work with Engineering and Product to translate scientific advances and real-world problems into product capabilities.
- Improve research and MLOps infrastructure for experimentation, reproducibility, deployment, monitoring, and iteration.
- Develop novel methods, proprietary datasets, evaluation frameworks, patents, and research publications where useful.
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