Dice is the leading career destination for tech experts at every stage of their careers. Our client, Info Dinamica Inc, is seeking the following. Apply via Dice today! Role: Lead Machine Learning Engineer Location: Mountain View, CA (3 days a week onsite) (OR) Remote Job Type: W2 Contract Duration: 12 months Experience: Senior/Lead Level Short Overview of JD: Looking for a ML Engineer who will be working on the products related to seismic and well log data, identifying simple geologic characteristics of the data (faults, horizons), and working knowledge of the different subsurface data formats and types. Primary Skills: MLOps, Deep learning, GPU training and inference, Image models, Google Cloud 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 Vertex 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 cloud platforms, particularly Google Cloud Platform (Google Cloud Platform); experience with edge deployments is a plus.

Additional (Nice to Have) Skills:

• Experience with Google Cloud 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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