Applied Machine Learning Researcher
Conduct applied research and engineering to improve language-model behavior in real-time voice conversations. The role focuses on fine-tuning, rigorous evaluation, production failure analysis, data strategies, and safely deploying improvements.
About the job
Responsibilities
- Research and improve LLM behavior for real-time voice conversations.
- Design and run fine-tuning experiments across data, model, and evaluation strategies.
- Build evaluation frameworks for model quality, workflow-following, naturalness, reliability, task completion, and overall conversation quality.
- Analyze production conversations to identify failure modes and improvement opportunities.
- Develop data curation, labeling, and synthetic data strategies.
- Compare model architectures, training approaches, prompts, and datasets.
- Investigate regressions and clearly explain why model behavior improves or degrades.
- Work with engineering to deploy research improvements safely and efficiently.
- Help define model release criteria, evaluation gates, and quality benchmarks.
Requirements
- Strong experience with machine learning and modern language models.
- Hands-on experience fine-tuning, evaluating, or adapting language models.
- Strong Python skills and comfort working with messy, real-world datasets.
- Ability to design rigorous experiments and interpret results clearly.
- Experience building or improving evaluation systems for AI models.
- Strong analytical skills and ability to debug model behavior.
- Clear written and spoken communication with technical and non-technical teammates.
- Practical focus on production impact, latency, reliability, and customer outcomes.
Nice to Have
- PhD in machine learning, NLP, or a related field.
- Experience with conversational AI, voice AI, or customer-support automation.
- Experience with SFT, preference tuning, DPO, RFT, GRPO, RLHF, LoRA, or QLoRA.
- Familiarity with model serving, inference optimization, or vLLM.
- Experience with synthetic data generation and data quality pipelines.
- Experience in a startup or fast-moving product environment.
Skills
Machine Learning, LLMs, Python, Fine-Tuning, Model Evaluation, NLP, Sft, Dpo, RLHF, Lora, Qlora, vLLM, Synthetic Data, Model Serving, Inference Optimization
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