Salary Range: 160000 to 190000 (Currency: USD) (Pay period: per-year-salary)

What You’ll Do

• Develop and Maintain ML Pipelines: Design, implement, and manage scalable machine learning pipelines to support various ML models from development to production. 
• Automate Workflows: Create automated workflows for data preprocessing, model training, evaluation, and deployment. 
• Monitor and Optimize: Continuously monitor the performance of ML models in production, ensuring they meet performance and accuracy standards. Optimize models and pipelines for efficiency. 
• Collaborate with Teams: Work closely with data scientists, software engineers, and other stakeholders to integrate ML solutions into existing systems and applications. 
• Ensure Compliance and Security: Implement best practices for data security and compliance, ensuring that all ML operations adhere to relevant regulations and standards. 
• Documentation and Reporting: Maintain comprehensive documentation of ML processes, workflows, and systems. Provide regular reports on model performance and system health. 

About You

• Deep knowledge of traditional ML concepts (e.g., LSTMs, RNNs, GMMs, SVMs, trees, boosting) as well as more recent deep learning fundamentals and NLP-related experience with word embeddings 
• Proficiency in JVM languages 
• Familiarity with CI/CD tools and methodologies 
• Proficiency with containerization (e.g., Docker) and orchestration tools 
• Experience with cloud-based ML platforms (e.g., Amazon Sagemaker) 
• Experience with common JVM search, linguistics, and other language frameworks (e.g., Lucene, StanfordNLP, OpenNLP, SparkNLP, ANTLR) 
• Experience using a Deep Learning Framework (e.g., Tensorflow, PyTorch, Keras) 
• Mature theoretical grasp of different neural networks on large-scale datasets  
• Deep and Fundamental understanding in signal processing concepts 
• A positive, collaborative, can-do attitude and a strong sense of ownership. 
• Familiarity with clinical data, concepts and language 
• Experience in model training automation with a combination of Supervised, Unsupervised, and Reinforcement methods

Bonus Points

• Prior remote working experience.   
• Worked in the healthcare domain.  Experience with healthcare clinical data modeling a strong bonus (e.g., OMOP, FHIR) 
• Experience with Transfer Learning, Transformers (e.g., BERT & ELMO), and Multilingual NLP 
• Familiarity with performance and load testing automation tools (e.g., Gatling, K6, Jmeter) and methodologies