AI Research Engineer

AI Research Engineer (ML Researcher) Who We Are Welcome to TELUS Digital — where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS , one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting-edge technology, agile thinking, and a people-first culture. With a global team across North America, South America, Central America, Europe, Africa, and APAC, we offer end-to-end expertise across eight core service areas: Digital Product Consulting, Digital Marketing Services, Data & AI, Strategy Consulting, Business Operations Modernization, Enterprise Applications, Cloud Engineering, and QA & Test Engineering. From mobile apps and websites to voice UI, chatbots, AI, customer service, and in-store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi-billion-dollar parent company. Location and Flexibility This role can be fully remote for candidates based in Brazil, due to team distribution and occasional in-person opportunities. If you are based in São Paulo or Porto Alegre, you are welcome to work from one of our offices on a flexible schedule. Core Mission: To independently own and execute a specific research task or sub-project, delivering high-quality experimental results and contributing novel findings to the team's core assets. Key Responsibilities: Experiment Design: Design and implement complex experiments to test new hypotheses, including defining evaluation protocols and baseline comparisons. Independent Research: Independently manage a research sub-task from start to finish, analyzing and interpreting results to draw clear conclusions. Code Contribution: Contribute high-quality, reusable code to the team's "reference implementation" repository. Benchmark Contribution: Actively contribute to improving the internal benchmark by identifying data gaps, proposing new evaluation metrics, or adding new models for comparison. Key Qualifications: Educational Background: PhD or Master's degree in Computer Science, Computational Linguistics, Machine Learning, or a related quantitative field. Industry Experience: 3+ years of hands-on experience in applied NLP research or ML engineering, ideally within a research lab or a data-centric AI environment. Ambiguity Tolerance: The ability to operationalize subjective concepts (e.g., "Creativity," "Safety," "Truthfulness") into concrete, annotatable guidelines. Research Communication: Proven track record of translating complex technical requirements into clear instructions for non-technical stakeholders (e.g., explaining "reasoning traces" to domain expert annotators). Technical Qualifications: Deep theoretical and practical understanding of Transformer architectures (Decoder-only GPT styles, Encoder-Decoder T5 styles), Attention mechanisms, Positional Embeddings, and Tokenization strategies (BPE, SentencePiece). Extensive experience with the post-training stack: Supervised Fine-Tuning (SFT) and Preference Alignment techniques including RLHF (PPO) and DPO (Direct Preference Optimization). Experience with noisy label handling, crowd-sourcing aggregation models (e.g., Dawid-Skene), active learning sampling strategies, and identifying semantic bias in large-scale datasets. You understand Function/Tool Calling, ReAct frameworks, and how to evaluate "trajectory" quality (reasoning steps) rather than just final output accuracy. You have experience designing LLM-as-a-Judge pipelines, pairwise comparison (Side-by-Side) systems, and reference-free metrics to measure Faithfulness, Coherence, and Safety. Proven ability to design "Data Evolution" pipelines (e.g., Evol-Instruct, Self-Instruct). You understand techniques for Knowledge Distillation and how to mitigate "Model Collapse" when training on synthetic data. Familiarity with adversarial testing. You can design prompts to stress-test safety filters (jailbreaking) and understand the trade-offs between helpfulness and harmlessness (False Refusal rates). Expert-level fluency in Python and deep learning libraries (PyTorch, TensorFlow, JAX). Ability to write robust scripts for processing massive text corpora (JSONL manipulation, RegEx, deduplication at scale). Experience with data versioning tools (DVC, LakeFS) is a plus. Experience running controlled ablation studies. 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