Platform Engineer

We’re Blue River, a team of innovators driven to create intelligent machinery that solves monumental problems for our customers. We empower our customers – farmers, construction crews, and foresters - to implement safer and more sustainable solutions, driving increased profitability with less reliance on scarce labor. We believe that focusing on the small stuff – pixel-by-pixel and task-by-task - leads to big gains. 

Blue River Technology aligns with John Deere’s vision to “innovate on behalf of humanity” by quickly identifying and solving high-value, high-uncertainty challenges in AI, machine learning, computer vision, and robotics. BRT acts as a research and development flywheel, building not only new products but also new platforms that reliably create value for both Deere and its customers. From fully autonomous machines to highly precise farming equipment, BRT and Deere are partnering to create technical breakthroughs in industries like agriculture and construction. 

Our people are at the heart of what we do. Through cross-disciplinary collaboration, this mission-driven team is eager to define the new frontier of robotics. We are always asking hard questions, rapidly iterating, and getting our boots in the field to figure it out. We won’t give up until we’ve made a tangible and positive impact on the planet!

Blue River Technology is based in Santa Clara, CA. 

Summary

We are looking for a senior software engineer with a strong background in ML infrastructure and platform engineering who is passionate about building the foundational systems that enable machine learning teams to move faster. Rather than developing ML models, this role focuses on designing scalable platforms, developer tooling, and infrastructure that support the full ML lifecycle across cloud and on-premises environments.

• Employment Type: Full-Time
• Work Location: Remote in the United States. 
• Visa sponsorship is possible for this position. 

Job Responsibilities

A combination, not necessarily all-inclusive, of the following:

• Design, build, and operate scalable ML infrastructure and platform capabilities that support the full machine learning lifecycle across cloud and on-premises environments.
• Develop developer tooling, services, and infrastructure that enable ML and engineering teams to build, deploy, and operate production systems more efficiently.
• Independently lead complex technical initiatives from problem definition and architecture through implementation, production rollout, and ongoing operational ownership.
• Make sound architectural and engineering decisions that balance near-term delivery with the platform's long-term scalability, reliability, and maintainability.