S
Research Engineer
Accepting applicationsSeer · Palo Alto, CA
Full-Time Mid_senior AIMachine Learningaiatemachine learning
Posted
26 May
Category
Test
Experience
Mid_senior
Country
United States
Data Infrastructure Machine Learning Engineer
We are building advanced intelligent robotic systems designed to operate in complex real-world environments. Our team develops the full stack — from high-performance hardware and robotic systems to large-scale machine learning infrastructure and advanced foundation models that power autonomous behavior.
Backed by substantial funding and operating at the intersection of robotics, AI, and distributed systems, we are investing aggressively in research, infrastructure, hardware development, and large-scale deployment to bring general-purpose robotics into production.
We are seeking Data Infrastructure Machine Learning Engineers across senior to staff levels to build and scale the systems powering large-scale training data pipelines. This role focuses on infrastructure for ingestion, storage, indexing, retrieval, observability, and throughput optimization for massive multimodal datasets.
What You’ll Do
Build and Scale Data Infrastructure
Architect and operate high-throughput data infrastructure capable of processing and managing billions of video and multimodal data samples
Design scalable systems with strong guarantees around reliability, latency, and cost efficiency
Optimize distributed storage systems, metadata services, and cloud object storage for large-scale datasets
Develop Large-Scale Retrieval and Indexing Systems
Build efficient indexing and retrieval systems for rapid dataset querying, filtering, and iteration
Support research and production workflows requiring fast access to large multimodal datasets
Improve data access patterns and storage performance across distributed systems
Improve Observability and Reliability
Develop monitoring, alerting, and failure recovery systems for large-scale data pipelines
Build performance analysis and observability tooling to improve throughput and system reliability
Identify bottlenecks and optimize workload balancing across distributed compute and storage infrastructure
Manage Data Lifecycle and Reproducibility
Build systems for dataset versioning, lineage tracking, and reproducibility across training runs
Manage data artifacts and metadata consistency throughout the ML lifecycle
Develop internal tooling and interfaces that enable engineers and researchers to explore and analyze large datasets efficiently
Support ML and VLM Integration
Integrate and scale vision-language model (VLM) inference workloads within distributed data pipelines
Support data enrichment, filtering, metadata generation, and automated labeling workflows
Collaborate closely with ML systems and research teams to improve training data quality and iteration speed
What We’re Looking For
5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or related areas
Strong experience building and operating large-scale data pipelines and distributed systems
Deep understanding of:
Distributed systems architecture
Databases and metadata systems
Indexing and retrieval strategies
Cloud storage architectures
Experience optimizing throughput, workload balancing, and cost-performance tradeoffs in cloud environments
Hands-on experience with distributed processing frameworks such as Ray or Spark
Strong observability, monitoring, and production reliability experience
Strong software engineering fundamentals with the ability to own systems end-to-end
Level Expectations
Senior engineers are expected to execute complex systems work with strong technical fundamentals and growing ownership
Staff-level engineers are expected to define architectural direction, drive technical strategy, and independently own major infrastructure decisions
Preferred Experience
Experience managing large multimodal datasets
Familiarity with ML training workflows and data lifecycle management
Experience running large-scale inference workloads in distributed or cloud environments
Familiarity with vision-language models (VLMs)
Experience with robotics data formats or real-world sensor data such as video, telemetry, or teleoperation logs
Familiarity with data warehouse technologies such as Snowflake, BigQuery, or Redshift
Experience with data versioning and lineage tooling such as DVC, Delta Lake, or similar systems
Why This Role Matters
Build the foundational data infrastructure that directly impacts model quality and research velocity
Work closely with ML systems and research teams on problems with immediate and measurable impact
Operate with high ownership in a small, highly technical environment
Help power intelligent robotic systems operating in real-world environments at scale
About the Company
We are a research-driven AI and robotics company focused on building scalable intelligent systems capable of robust real-world operation. By combining advances in machine learning, robotics, distributed systems, and infrastructure engineering, we aim to push the frontier of embodied intelligence.
We are committed to building an inclusive and diverse workplace and encourage applicants from all backgrounds to apply.
Show more Show less
We are building advanced intelligent robotic systems designed to operate in complex real-world environments. Our team develops the full stack — from high-performance hardware and robotic systems to large-scale machine learning infrastructure and advanced foundation models that power autonomous behavior.
Backed by substantial funding and operating at the intersection of robotics, AI, and distributed systems, we are investing aggressively in research, infrastructure, hardware development, and large-scale deployment to bring general-purpose robotics into production.
We are seeking Data Infrastructure Machine Learning Engineers across senior to staff levels to build and scale the systems powering large-scale training data pipelines. This role focuses on infrastructure for ingestion, storage, indexing, retrieval, observability, and throughput optimization for massive multimodal datasets.
What You’ll Do
Build and Scale Data Infrastructure
Architect and operate high-throughput data infrastructure capable of processing and managing billions of video and multimodal data samples
Design scalable systems with strong guarantees around reliability, latency, and cost efficiency
Optimize distributed storage systems, metadata services, and cloud object storage for large-scale datasets
Develop Large-Scale Retrieval and Indexing Systems
Build efficient indexing and retrieval systems for rapid dataset querying, filtering, and iteration
Support research and production workflows requiring fast access to large multimodal datasets
Improve data access patterns and storage performance across distributed systems
Improve Observability and Reliability
Develop monitoring, alerting, and failure recovery systems for large-scale data pipelines
Build performance analysis and observability tooling to improve throughput and system reliability
Identify bottlenecks and optimize workload balancing across distributed compute and storage infrastructure
Manage Data Lifecycle and Reproducibility
Build systems for dataset versioning, lineage tracking, and reproducibility across training runs
Manage data artifacts and metadata consistency throughout the ML lifecycle
Develop internal tooling and interfaces that enable engineers and researchers to explore and analyze large datasets efficiently
Support ML and VLM Integration
Integrate and scale vision-language model (VLM) inference workloads within distributed data pipelines
Support data enrichment, filtering, metadata generation, and automated labeling workflows
Collaborate closely with ML systems and research teams to improve training data quality and iteration speed
What We’re Looking For
5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or related areas
Strong experience building and operating large-scale data pipelines and distributed systems
Deep understanding of:
Distributed systems architecture
Databases and metadata systems
Indexing and retrieval strategies
Cloud storage architectures
Experience optimizing throughput, workload balancing, and cost-performance tradeoffs in cloud environments
Hands-on experience with distributed processing frameworks such as Ray or Spark
Strong observability, monitoring, and production reliability experience
Strong software engineering fundamentals with the ability to own systems end-to-end
Level Expectations
Senior engineers are expected to execute complex systems work with strong technical fundamentals and growing ownership
Staff-level engineers are expected to define architectural direction, drive technical strategy, and independently own major infrastructure decisions
Preferred Experience
Experience managing large multimodal datasets
Familiarity with ML training workflows and data lifecycle management
Experience running large-scale inference workloads in distributed or cloud environments
Familiarity with vision-language models (VLMs)
Experience with robotics data formats or real-world sensor data such as video, telemetry, or teleoperation logs
Familiarity with data warehouse technologies such as Snowflake, BigQuery, or Redshift
Experience with data versioning and lineage tooling such as DVC, Delta Lake, or similar systems
Why This Role Matters
Build the foundational data infrastructure that directly impacts model quality and research velocity
Work closely with ML systems and research teams on problems with immediate and measurable impact
Operate with high ownership in a small, highly technical environment
Help power intelligent robotic systems operating in real-world environments at scale
About the Company
We are a research-driven AI and robotics company focused on building scalable intelligent systems capable of robust real-world operation. By combining advances in machine learning, robotics, distributed systems, and infrastructure engineering, we aim to push the frontier of embodied intelligence.
We are committed to building an inclusive and diverse workplace and encourage applicants from all backgrounds to apply.
Show more Show less