What are the responsibilities and job description for the Software Engineer III / Senior Machine Learning Engineer position at TalentBurst, an Inc 5000 company?
Senior Machine Learning Engineer --- Machine Translation Automation
The role will be Onsite.
Job Summary
Play a part in the next revolution in human-computer interaction. Build groundbreaking technology for large scale systems, spoken language, big data, and artificial intelligence. The AI/ML - Machine Translation team is looking for exceptional Machine Learning Engineers passionate about delighting customer's experience, building and improving the Machine Learning Automation and Tooling with a strong focus on model automation pipelines development and deployment.
Qualifications
• Strong machine learning expertise with hands-on experience in model fine-tuning, evaluation, experiment tracking, pipeline building and deployment
• 3 years working experience in ML lifecycle, Model management, big data/Spark/MapReduce, large distributed system, cloud computing etc
• Proficient coding skills in Python and experience with ML infrastructure tools
• Excellent communication and problem solving skills
• Experience with LLMs, neural machine translation is a plus
• Deep knowledge in ML frameworks and technologies such as NLP, MT, ASR, PyTorch, TensorFlow, JAX, and transformer architectures is a plus
Description
You will be part of a team that's responsible for a wide variety of language technologies related development activities. Your focus will be on developing the model automation pipelines which are highly scalable, robust and efficient. The role will be part of the model automation team to deal with large quantities of data, apply the state-of-the-art methods in deep learning to tackle real world problems, create the production quality models at scale and set up ML CI/CD pipelines.
Key responsibilities include:
• Developing automation pipelines and tools for training, evaluating and deploying machine learning models for machine translation and related NLP tasks
• Implementing and optimizing ML pipelines with emphasis on distributed data processing, training and efficiency
• Collaborating with software engineers and QE to integrate ML models into production systems
Education
B.S. or M.S. in Computer Science, Machine Learning, Statistics, or related field.