Accountabilities:

As an Applied Machine Learning Researcher, you will take ownership of improving AI model performance through applied research, experimentation, and production-focused development. You will investigate real-world challenges, create evaluation frameworks, and collaborate with technical teams to continuously enhance conversational AI capabilities.

• Research and improve the behavior of large language models for real-time voice interactions and customer workflows.
• Design, execute, and analyze fine-tuning experiments involving datasets, training approaches, evaluation methods, and model strategies.
• Build and maintain evaluation frameworks measuring quality, reliability, naturalness, workflow adherence, and task completion.
• Analyze production conversations to identify model limitations, failure patterns, and opportunities for improvement.
• Develop data strategies including data curation, labeling approaches, and synthetic data generation.
• Compare different model architectures, prompts, training techniques, and datasets to optimize performance.
• Investigate model regressions and clearly communicate the reasons behind improvements or declines.
• Partner with engineering teams to safely deploy research advancements into production systems.
• Contribute to model release standards, evaluation benchmarks, and quality criteria.

Requirements:

The ideal candidate is a hands-on machine learning professional who combines strong technical expertise with a practical mindset focused on delivering measurable improvements in production AI systems.

• Strong experience in machine learning and modern language model technologies.
• Hands-on experience fine-tuning, evaluating, or adapting large language models.
• Advanced Python skills and the ability to work effectively with complex, real-world datasets.
• Experience designing rigorous experiments and interpreting results to guide decisions.
• Proven ability to build or improve AI model evaluation systems.
• Strong analytical and debugging skills, with the ability to understand and improve model behavior.
• Excellent written and verbal communication skills, with the ability to explain technical findings clearly.
• A product-oriented approach focused on real-world performance, latency, reliability, and customer outcomes.
• Experience with conversational AI, voice AI, or customer automation is a plus.
• Familiarity with techniques such as SFT, preference tuning, DPO, RFT, GRPO, RLHF, LoRA, or QLoRA is advantageous.
• Knowledge of model serving, inference optimization, vLLM, or synthetic data pipelines is beneficial.
• A PhD in machine learning, NLP, or a related discipline is considered a plus.
• Experience in startup or fast-paced product environments is valued.

Benefits:

• Fully remote working environment with flexibility for candidates based in Australia.
• Opportunity to work on advanced AI technology with direct production impact.
• High ownership role where research outcomes are deployed into real customer solutions.
• Collaboration with talented engineers, researchers, and product specialists.
• Opportunities to grow within a fast-moving AI-focused organization.
• Regular opportunities for team connection and in-person gatherings.
• A culture that values curiosity, ambition, collaboration, and continuous improvement.