Senior ML/NLP Engineer

About the Company

A privately funded Australian AI startup building small, domain specific language models for a research discovery and intelligence platform. The team is small by design, deeply technical, and product focused. You would be joining alongside PhD level researchers, helping turn research into deployed, commercial products. Every hire has direct influence on the product, the architecture, and the direction of the company.

About the Role

This is a hands on Senior ML/NLP Engineer role at the core of the platform's intelligence layer. The research side is well covered. What the team needs is someone who can take NLP concepts and ship them as production systems. You will design, build and deploy the text processing and language systems that power the platform. You will work closely with researchers and engineers, translating ideas into robust, scalable, cost effective solutions. This is one of a maximum of two hires for this function.

Responsibilities

• Design and implement NLP pipelines for research discovery, including text classification, clustering, NER, and information extraction
• Build and maintain production NLP workflows across both classical and language model based approaches
• Evaluate and benchmark methods systematically, choosing the right tool for each problem
• Deploy and productionise models with a focus on performance, cost, and reliability
• Collaborate with PhD level researchers to bridge the gap between research and commercial deployment
• Communicate methodology choices clearly to technical and non technical stakeholders

Qualifications

• 5 to 10 years of experience in software engineering with a genuine specialisation in NLP, text processing, or computational linguistics
• Practical experience with text classification, clustering, NER, information extraction, semantic search, or similar
• Strong Python engineering skills with experience building production systems, not just notebooks
• Experience deploying NLP systems in cloud environments
• Sound judgement on when to use classical NLP versus language model based approaches
• A hands on builder who ships working systems, not just prototypes
• Someone who thrives in a small, collaborative team where cultural fit matters as much as technical skill

Required Skills

• Experience with LLMs, RAG architectures, or fine tuning (LoRA/QLoRA)
• Embedding models, model compression, or knowledge distillation
• C\+\+, Rust, or Go alongside Python
• Vector databases (pgvector, Weaviate, Qdrant, Pinecone)
• Graph databases and knowledge graphs (Neo4j)
• Scientific text processing, bibliometric analysis, or research evaluation
• Fluency in languages other than English

Preferred Skills

• Your background is primarily computer vision, robotics, general ML, or pure data science
• Your experience is mainly agent orchestration or LLM API wrappers without deeper NLP foundations
• You are looking for a team management role rather than hands on engineering