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W2 - 15+ Senior Machine Learning Engineer (Image processing & Video recognition)

ProhiresMountain View, CA🇺🇸United StatesPosted 26 Aug 2026

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Mountain View, CA, United States
Posted
Yesterday
Machine LearningDeep LearningGoogle CloudPyTorchTensorFlow

Job Description

Position

Senior Machine Learning Engineer

Main skills

ML Ops, image models, deep learning, GPU training, Google Cloud Platform, TensorFlow, PyTorch, Agentic coding tools

Short overview

We are looking for an ML Engineer who will be working on products related to seismic and well log data, identifying simple geologic characteristics of the data (faults, horizons). Full stack ML with the focus on signal processing - image and time series.

Employment type

W2

Project duratio

12 months

Location

Mountain View, CA or Remote

Work mode

3 days per week from office if onsite in Mountain View or remote

Travel

No

Recruitment process

General -> Technical Interview -> Client Interview

Required start date

ASAP

Level

Senior level  

Work authorization statu

H1B and TN visa candidates can be considered. Any applicant needs to be on your W2.

 
We are looking for an ML Engineer who will be working on products related to seismic and well log data, identifying simple geologic characteristics of the data (faults, horizons). Full stack ML with the focus on signal processing - image and time series.
We are looking for a candidate who:
1. Versed in deep learning, GPU training and inference, image models.
2. 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).
Requirements:
· 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.
Additional (nice to have) skills:
· 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.

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