Cloud - ML Platform Engineer

Join the team bringing advanced autonomy to the built world At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $80M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. The Role: We are looking for Senior or Staff level ML Platform Engineers to join our team in either New York or San Francisco. At Bedrock Robotics, ML Platform Engineering bridges infrastructure and machine learning. We build modern systems for high scale training of end-to-end autonomy, focus on system performance for GPUs, and build solutions for data mining, metrics, labeling, experiment tracking, and evaluation. We are the engine that powers Bedrock Robotics’ fast-moving and novel physical world autonomy. As a passionate ML Platform engineer, you will have an immediate impact on our autonomy progress. We are looking for experienced Senior Software Engineers / TLs experienced with ML platforms and who can work closely with ML engineers and other infrastructure teams. Also, you get to drive 100,000 lb excavators. What you'll do: • Build out robot data labeling and evaluation pipelines. • Design and create a data mining solution. • Iterate and evolve experiment tracking. • Improve performance of our scaled training loop. • Work with other ML teams to understand their workflows and their needs. • Develop, maintain, and enhance frameworks for AI/ML model development and deployment while establishing and driving best practices in MLOps • Design, advocate, and implement for usability, reliability, scalability, operational excellence, and cost management while delivering incrementally. • Collaborate closely with ML Engineers, Data Scientists, Data Engineers and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process. Required Qualifications: • 5+ years of professional software engineering experience building ML platforms, infrastructure, or internal tooling that accelerates model development and deployment for ML engineers. • 4+ years of experience (may include graduate research) as an ML, Data, Platform, or Distributed Systems Engineer working on large-scale, complex, or highly distributed systems. • 2+ years of hands-on experience designing, building, and operating production-grade ML systems, such as training pipelines, feature stores, model serving, evaluation frameworks, or workflow orchestration. • Proven experience leading projects end-to-end, from concept and initial design through implementation and deployment, working with at least one other engineer. • 2+ years of close collaboration with cross-functional ML stakeholders to understand requirements and ship platform capabilities that improve iteration speed and reliability. Preferred Skills/Qualifications: • Experience in performance optimization for GPUs • Experience working with multi-modal data is a plus • Startup experience is a plus • Experience with robotics, simulation, or perception ML pipelines • Familiarity with modern ML infra stacks (Ray, Kubeflow, MLflow, Metaflow, Airflow, Feast, Vertex, SageMaker, etc.) • Hands on experience in distributed training, model optimization, or experiment tracking systems • Experience building internal developer platforms or self-service tooling Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.