Geospatial Cloud Architect
3 days ago
Overview of Job Responsibilities
We are looking for an experienced Geospatial AI/ML Engineer to join our team at Wherobots. As a key member of our WherobotsDB & WherobotsAI team, you will be responsible for designing and building our ML and AI platform.
This involves defining, implementing, and maintaining the systems and data architecture for our ML and AI platform. You will build technology around distributed geospatial model training and inference, integrated into our products.
This includes building, training, and fine-tuning geospatial foundation models. You will collaborate closely with cross-functional teams to define requirements, identify technical solutions, and deliver high-quality software.
We encourage open-source contributions to Apache Sedona, PyTorch, and other relevant software packages.
Qualifications and Requirements
- Strong Technical Foundation: 5+ years of hands-on experience implementing geospatial AI/ML pipelines and systems.
- Proficiency in Python: Experience with AI/ML frameworks like PyTorch or TensorFlow required.
- Proven Track Record: A proven track record of building high-quality and maintainable software, and delivering AI/ML-enabled products and features to enterprise customers in production.
- Solid Understanding: Solid understanding of cloud-deployed software architectures, distributed systems, data structures, and algorithms.
- Excellent Communication: Strong communication and collaboration abilities, with a proven track record of working effectively in a hybrid or distributed team environment.
- Excellent Problem-Solving: Excellent problem-solving and troubleshooting skills.
Benefits and Compensation
We offer a competitive compensation package, including a base salary range of $175k-$250k per year, equity, and a comprehensive benefits package.
We provide flexibility and choice in the working arrangement for most roles, including remote and/or in-office roles. Please note that the base pay range is a guideline and may vary based on factors such as work location, seniority, skills, and experience of the candidate.
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