Machine Learning Engineer

Role & responsibilities

Model Development & Optimization

• Train, fine-tune, and evaluate speech recognition (ASR), LLM, and NLU models
• Contribute to model monitoring, evaluation pipelines, and test harnesses
• Optimize for speed, robustness, and market variability

Deployment & Integration Support

• Collaborate with MLEs and DevOps to validate models in staging and production
• Participate in benchmarking across markets and environments (hardware, accents, menu dynamics)
• Help investigate production issues and assist in triage/debugging

Collaboration & Experimentation

• Work with AI Engineers to deploy prompt-based systems enhanced with learned representations
• Pair with QA to improve model accuracy and regressions
• Contribute to experiment design, data selection, and annotation loops

Preferred candidate profile

• 4-8 years of experience in applied machine learning or MLE roles.
• Proficiency in Python and ML libraries such as PyTorch, TensorFlow, HuggingFace.
• Experience in fine-tuning speech, ASR, or embedding models.
• Deep experience with ML pipelines and model lifecycle management.
• Hands-on with NLP/LLM modeling
• Fine-tuning (LoRA/QLoRA)
• Embeddings
• Prompt optimization
• Exposure on building evaluation frameworks for models
• Experience with ML ops elements:
• Model tracking (MLflow or similar)
• Experimentation workflows
• Versioning + testing
• AWS experience for model training + hosting
• Ability to work on:
• Model optimization
• Latency/throughput tuning
• Scalable inference architecture