LLM Direction Algorithm Engineer

9 hours ago

Singapore X STAR TECHNOLOGY PTE. LTD. Full time

Key Responsibilities Design and implement algorithms that steer or direct LLM output such as: controlling tone/voice/persona, enforcing constraints (e.g., style, accuracy, safe behavior), prompting/control flow, tool-use by the model, and multi-step reasoning workflows. Work on post-training processes such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), reward modelling and alignment techniques to shape model behavior. For example, one job listing describes "advanced post-training of large language models (e.g. SFT, RLHF/RLAIF...)". Build and maintain evaluation & benchmark pipelines for behavior: e.g., factuality, bias/ethics, latency, throughput, tool-calls, JSON-schema compliance, multi-round interaction, hallucination detection. For instance: "Build offline and live eval pipelines for alignment, factuality, grounding, and hallucinations." Optimize inference architecture and serve models at scale: e.g., context window management, checkpoint routing, efficient serving, adapter/LoRA training, distributed training/inference frameworks. As one role states: "Design, deploy, and operate Model Context Protocol (MCP) servers ... manage context windows." Collaborate with research, product, safety, and infrastructure teams to integrate directed-LLM capabilities into product experiences-ensuring the model's behavior meets user intent, product goals, and safety/ethics standards. Investigate and adopt state-of-the-art techniques in LLM alignment, prompting/steering, retrieval-augmented generation (RAG), multi-modal integration, agentic behavior, and emergent capabilities. What We're Looking For / Qualifications Required: MS or PhD in Computer Science, Machine Learning, NLP, AI, or equivalent experience. Strong programming skills (Python) and experience with ML frameworks (e.g., PyTorch, TensorFlow) as well as working knowledge of Transformer‐based LLMs (e.g., LLaMA, GPT‑4, Falcon, etc.). Solid understanding of NLP fundamentals (language modelling, sequence-to-sequence, embeddings, attention mechanisms) and experience in tasks such as question-answering, summarisation, dialogue, retrieval. Experience with at least one of: fine-tuning LLMs, RLHF, alignment work, prompt engineering, RAG setups. Good software engineering skills: end-to-end system building (data pipelines, model training, serving/inference, evaluation). Strong analytical/problem-solving skills and ability to work cross-functionally in a fast-moving environment. Preferred: Hands-on experience with distributed training/inference frameworks: e.g., DeepSpeed, FSDP, vLLM, Triton. Experience with tool‐use by LLMs, function-calling, agents, multi‐step reasoning workflows-e.g., "aligning models for reliable JSON-schema function calls and external tool usage". Familiarity with safety, bias/ethics, fairness, adversarial robustness in LLMs. Experience with multi-modal (text+image+code) or agentic interfaces. Publications in top ML/NLP/AI conferences (ICLR, NeurIPS, ACL, EMNLP) or strong open-source contributions.

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