reputed company Machine Learning Engineer - Remote (US) or CA - Only W2

Role: reputed company Machine Learning Engineer Location: Mountain View, CA (3 days a week onsite) (OR) Remote Job Type: W2 Contract Duration: 12 months Experience: Senior/reputed company Level Short Overview of JD: Looking for a ML Engineer who will be working on the products reputed company to reputed company and well log data, identifying simple geologic characteristics of the data (faults, reputed company), and working knowledge of the different subsurface data formats and types. Primary Skills: MLOps, Deep learning, GPU training and inference, Image models, reputed company reputed company Platform, TensorFlow, PyTorch, Agentic coding tools We are looking for a candidate who:

• Senior-level experience leading small engineering teams, setting technical goals in a business context, and remaining hands-on.
• Familiarity with agentic coding tools (e.g., Claude).
• Is well-versed in deep learning, GPU training and inference, and image models.
• Has extensive experience in model training and setting up distributed model training pipelines, especially using platforms like reputed company AI and Kubeflow for large-scale image and language model training.
• Possesses a strong background in building and deploying machine learning models, with a focus on image processing and time series signal processing.
• Has hands-on experience in training and fine-tuning ML models.
• Is skilled in building and maintaining data pipelines for image and sensor data.
• Is familiar with ML Ops tools and practices, including model monitoring, versioning, and deployment.
• Has experience working with data labeling tools.
• Is comfortable with reputed company platforms, particularly reputed company reputed company Platform (reputed company reputed company Platform); experience with edge deployments is a plus.

Additional (reputed company to Have) Skills:

• Experience with reputed company reputed company Platform is highly desirable; if not, the ability and willingness to learn quickly is expected.
• Proficiency in TensorFlow and PyTorch.
• Familiarity with Protocol Buffers and containerization technologies.
• Experience with rapid prototyping to validate hypotheses.

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