Lead Machine Learning Engineer

About

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.

Responsibilities

• Work on products related to seismic and well log data.
• Identify simple geologic characteristics of the data such as faults and horizons.
• Lead small engineering teams and set technical goals in a business context.
• Remain hands-on in engineering tasks.
• Build and deploy machine learning models focusing on image processing and time series signal processing.
• Train and fine-tune ML models.
• Build and maintain data pipelines for image and sensor data.
• Set up distributed model training pipelines using platforms like Vertex AI and Kubeflow.
• Use ML Ops tools and practices including model monitoring, versioning, and deployment.
• Work with data labeling tools.

Qualifications

• Senior-level experience leading small engineering teams.
• Familiarity with agentic coding tools (e.g., Claude).
• Well-versed in deep learning, GPU training and inference, and image models.
• Extensive experience in model training and distributed model training pipelines.
• Strong background in building and deploying machine learning models.
• Hands-on experience in training and fine-tuning ML models.
• Skilled in building and maintaining data pipelines for image and sensor data.
• Familiarity with ML Ops tools and practices.
• Experience working with data labeling tools.
• Comfortable with cloud platforms, particularly Google Cloud Platform (GCP).

Additional Skills (Nice to Have)

• Experience with GCP 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.