Applied Machine Learning Researcher

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.