Job Opportunities API

AI ENGINEER (CONTEXT ENGINEERING) WHO WE ARE Welcome to TELUS Digital https://www.telusdigital.com/— where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS https://www.telus.com/en/, 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 underst