LLM Direction Algorithm Engineer

4 days ago

Central Region, Singapore X Star Technology Pte Ltd Full time $120,000 - $200,000 per year

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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