Senior Machine Learning Engineer - NLP

What you’ll do day to day

• ML Development: Engage in the full lifecycle of machine learning model development, from initial concept and data collection to training, evaluation, and deployment in production environments.
• Solution Crafting: Work closely with our partners and product managers to understand their specific problems and develop tailored ML solutions to address these challenges effectively.
• Innovation and Research: Stay abreast of the latest advancements in ML, DL, and NLP. Introduce and evangelize new technologies and methodologies that can benefit the Observe.AI ecosystem.
• High Stakes in Production: The systems you design are critical to our customers' operations, requiring resilience and low-latency performance. Your role will focus on ensuring these systems are reliable and predictable in production, minimizing downtime.
• Community Engagement: Contribute to the broader ML community through technical blogs and publications, enhancing both your and the company's presence in the field.

What you’ll bring to the role

• Educational Background: Master’s or Ph.D degree in Computer Science or a related discipline with a strong foundation in ML, DL, and NLP.
• Professional Experience: At least 4 years of industry experience in building and scaling large-scale NLP systems.
• Technical Expertise: Deep understanding of ML and NLP fundamentals, with practical experience in deploying machine learning models in production. Proficiency in Python and ML frameworks like Pytorch, TensorFlow, TensorRT and Sagemaker/VertexAI.
• Knowledge of NLP Trends: Solid grasp of recent trends in NLP, including transformers, language models, text classification, generative approaches, entity recognition, prompting techniques, question answering, information retrieval, and more.
• Soft Skills: Excellent communication, collaboration, and presentation skills are essential, as is the ability to provide thought leadership and influence product development.
• Additional Qualifications: Experience with Spoken Language Understanding or Conversational AI, publications in top NLP conferences, and relevant open-source contributions are desirable.