Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Senior Machine Learning Engineer to develop products using seismic and well log data to identify geologic characteristics such as faults and horizons. The role focuses on full-stack machine learning, signal processing for image and time-series data, distributed model training, and ML operations.
Responsibilities
- Versed in deep learning, GPU training and inference, image models
- Seasoned in model training and setting distributed model training pipelines. Specifically using Vertex, Kubeflow, etc for training of larger models on large amounts of data (Image, language)
- Strong experience in building and deploying machine learning models, with a focus on image processing and time series signal processing
- Experience in training and fine-tuning ML models
- Experience in building and maintaining data pipelines for image and other sensor data
- Experience with ML Ops tools and practices, such as model monitoring, versioning, and deployment
- Experience in working with data labeling tools
- Experience with cloud platforms, Google Cloud Platform in particular. Experience with edge deployments is a plus
- Excellent communication and collaboration skills
- Ability to work independently and as part of a team
- Strong problem-solving skills
- Passionate about machine learning and its applications
- Google Cloud Platform would be useful, if no such experience, then it''s expected that the candidate can quickly learn it before the start, and/or at the beginning of the engagement
- Proficiency in TensorFlow and PyTorch
- Protocol Buffers
- Containers
- Quick prototyping experience is a great plus. The team will need to build prototypes to validate hypotheses
Skills
- Versed in deep learning, GPU training and inference, image models
- Seasoned in model training and setting distributed model training pipelines. Specifically using Vertex, Kubeflow, etc for training of larger models on large amounts of data (Image, language)
- Strong experience in building and deploying machine learning models, with a focus on image processing and time series signal processing
- Experience in training and fine-tuning ML models
- Experience in building and maintaining data pipelines for image and other sensor data
- Experience with ML Ops tools and practices, such as model monitoring, versioning, and deployment
- Experience in working with data labeling tools
- Experience with cloud platforms, Google Cloud Platform in particular
- Excellent communication and collaboration skills
- Ability to work independently and as part of a team
- Strong problem-solving skills
- Passionate about machine learning and its applications
- Proficiency in TensorFlow and PyTorch
- Protocol Buffers
- Containers
- Experience with edge deployments is a plus
- Google Cloud Platform would be useful, if no such experience, then it''s expected that the candidate can quickly learn it before the start, and/or at the beginning of the engagement
- Quick prototyping experience is a great plus
Benefits
- Remote work is available.
- If onsite in Mountain View, work 3 days per week from the office.
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