Sr Platform Engineer, ML Infrastructure

Blue River Technology

6d•$160,000 - $287,000•Remote

About The Position

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. 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.

Requirements

5+ years of professional software engineering experience, with a focus on platform, infrastructure, or distributed systems.
Strong Python engineering skills, including building production services, SDKs, automation, or platform tooling.
Experience designing, building, and operating production platform capabilities used by multiple engineering teams.
Understanding of ML platform architecture and the end-to-end ML lifecycle, including experimentation, distributed training, model deployment, and production operations.
Experience building and operating applications on Kubernetes and cloud platforms (AWS preferred), with an understanding of production reliability, observability, and operational best practices.
Strong technical judgment with the ability to independently lead complex technical initiatives from discovery through production, collaborating effectively with ML engineers, infrastructure teams, and product stakeholders.

Nice To Haves

Experience building developer platforms, tooling, or internal services that improve engineering productivity and reduce operational complexity.
Experience with workflow orchestration or distributed compute technologies such as Airflow, Kubeflow, Ray, Spark, or similar systems.
Experience designing and optimizing distributed, GPU-intensive compute platforms for ML training, inference, or large-scale image processing.
Experience supporting production machine learning platforms in computer vision, robotics, or similar domains.
Demonstrated technical leadership through architecture, mentorship, or influencing technical direction across teams.

Responsibilities

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.
Build reliable, scalable, easy-to-use platform capabilities that improve developer productivity, simplify operations, and help engineering teams move faster.
Partner closely with ML engineers, infrastructure engineers, and other stakeholders to understand customer needs and translate them into effective platform solutions.
Identify and solve challenging infrastructure and platform problems, including opportunities to improve performance, reliability, scalability, and developer experience.
Drive adoption and continuous improvement of platform capabilities by incorporating feedback from the engineering teams that use them.
Establish a high bar for software quality, operational excellence, and production readiness across the systems and capabilities you own.
Deliver platform solutions with measurable engineering and business impact across multiple teams and use cases.