Overview
Hybrid
Depends on Experience
Contract - W2
Skills
Machine Learning (ML)
PyTorch
Time Series
2D/3D
Vision Models
ML Ops
GCP
Job Details
Position: Senior Machine Learning Engineer
Location: Mountain View, CA - Hybrid
Duration: 12 Months
Main skill: Time Series, ML models training production experience, Vision Models - 2D/3D
Role Overview:
The customer is looking for a Machine Learning Engineer to work on products related to seismic and well log data. The role will involve identifying simple geologic characteristics of the data (faults, horizons) and requires working knowledge of different subsurface data formats and types.
Requirements:
- Strong experience in building and deploying machine learning models, especially in image processing and time series signal processing.
- Proficiency with TensorFlow and PyTorch.
- Hands-on experience with training and fine-tuning ML models for production.
- Skilled in building and maintaining data pipelines for image and sensor data.
- Familiarity with ML Ops tools and practices (model monitoring, versioning, and deployment).
- Experience with data labeling tools.
- Knowledge of cloud platforms, particularly Google Cloud Platform (Google Cloud Platform). Edge deployment experience is a plus.
- Excellent communication and collaboration abilities.
- Strong problem-solving skills and passion for machine learning and its applications.
Nice-to-Have Skills:
- Quick learning ability for Google Cloud Platform if no prior experience (training support may be provided).
- Familiarity with Protocol Buffers, Containers, and rapid prototyping to validate hypotheses.
Key Focus Areas for Candidates:
- Time series and ML model training experience in production environments.
- Expertise in Vision Models (2D/3D).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.