Senior Data Scientist

Blackbook.ai; Brisbane; On-site; AUD 140,000 - 210,000; Full time

Job summary

Blackbook.ai is seeking a Senior Data Scientist / Machine Learning Consultant to lead full lifecycle ML projects—from problem framing and data strategy through model development, deployment, and ongoing performance management.

This role sits at the intersection of applied research and production engineering, requiring deep NLP and LLM expertise and the ability to translate business problems into scalable AI solutions.

Qualifications

• 4+ years of professional experience in ML engineering or applied data science with a strong NLP focus.
• Proven track record fine-tuning and deploying LLMs and transformer-based models in production environments.
• Deep proficiency in Python and PyTorch; working knowledge of C++ is an asset.
• Experience designing and evaluating RAG pipelines, including embedding model fine-tuning and vector stores (Milvus, Faiss, Pinecone).
• Proficiency with Docker, Git, Linux, and CI/CD practices in an ML context.

Responsibilities

• Lead ML projects from problem framing and data strategy to deployment.
• Develop and fine-tune LLMs and multimodal models; evaluate via rigorous experiments.
• Build and deploy FastAPI services wrapping model endpoints.
• Own ML Ops pipelines, experiment tracking, model versioning, monitoring drift.
• Mentor junior data scientists and collaborate with engineers and product teams.
• Represent work via publications or technical blogs when appropriate.

Skills

Python

PyTorch

NLP

LLMs

PEFT/LoRA

RAG pipelines

Vector stores

Docker

CI/CD

FastAPI

ML tooling

Education

Bachelor's and Master's degree in Computer Science/Data Science/Mathematics

Tools

vLLM

Triton Inference Server

TensorRT-LLM

ONNX Runtime

Milvus

Faiss

Pinecone

Megatron-LM

Docker

Git

Linux

Job description

Blackbook.ai is seeking a Senior Data Scientist / Machine Learning Consultant to lead the full lifecycle of machine learning projects — from problem framing and data strategy through to model development, deployment, and ongoing performance management.

This role sits at the intersection of applied research and production engineering, requiring both deep technical expertise in NLP and LLMs and the ability to translate business problems into scalable AI solutions.

KEY RESPONSIBILITIES

Model Development & Research

• Design, fine-tune, and evaluate LLMs and multimodal models (e.g. Qwen, LLaMA) using frameworks such as PyTorch, PEFT/LoRA, ms-swift, and Megatron-LM
• Develop and own RAG pipelines, embedding models, and retrieval systems (dense and sparse), including contrastive learning approaches to improve retrieval accuracy
• Lead applied NLP work across tasks including text classification, NER, summarisation, OCR, and code generation
• Conduct rigorous experimentation, benchmarking, and ablation studies; document findings clearly for technical and non-technical audiences

Production Deployment & MLOps

• Containerise and deploy models using vLLM, Triton Inference Server, TensorRT-LLM, ONNX Runtime, and Docker
• Build and maintain FastAPI services wrapping model inference endpoints; integrate models into production applications
• Own CI/CD pipelines for ML components; manage experiment tracking and model versioning (e.g. ClearML, MLflow)
• Monitor deployed models for drift, latency, and quality; iterate based on real-world feedback

Technical Leadership & Collaboration

• Act as a technical authority on LLM and NLP best practices across the data science team
• Collaborate closely with software engineers, product managers, and domain experts to scope, deliver, and iterate on AI features
• Conduct code reviews, define engineering standards, and contribute to a culture of technical excellence
• Mentor and guide junior and mid-level data scientists; support skill development through pairing and knowledge-sharing sessions

Research & Innovation

• Stay current with the latest advances in LLMs, multimodal AI, and NLP; evaluate and introduce new techniques with business relevance
• Represent the organisation's work through publications, conference papers, or technical blog posts where appropriate
• Contribute to the development and maintenance of internal benchmarks, evaluation harnesses, and datasets

QUALIFICATIONS & EXPERIENCE

Required

• 4+ years of professional experience in machine learning engineering or applied data science, with a strong NLP focus
• Proven track record fine-tuning and deploying LLMs and transformer-based models in production environmentsDeep proficiency in Python and PyTorch; working knowledge of C++ is an asset
• Hands-on experience with at least two of: vLLM, Triton Inference Server, TensorRT-LLM, ONNX Runtime
• Experience designing and evaluating RAG pipelines, including embedding model fine-tuning and vector stores (e.g. Milvus, Faiss, Pinecone)
• Solid understanding of PEFT methods (LoRA, QLoRA, adapters) and distributed training (Megatron-LM or equivalent)
• Proficiency with Docker, Git, Linux, and CI/CD practices in an ML context
• Bachelor's and Master's degree in Computer Science, Data Science, Mathematics, or a related technical field

Preferred

• Experience with multimodal models (vision-language, speech-language) and related benchmarks
• Publications or contributions to peer-reviewed NLP/ML conferences (e.g. ACL, EMNLP, NeurIPS, ICLR)
• Experience with code generation or domain-specific language (DSL) modelling tasks
• Familiarity with speech synthesis pipelines and text normalisation for TTS
• Experience mentoring junior engineers or delivering ML education in an academic or corporate context