Member of Technical Staff - ML Operations

Veeda AI; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team tackling challenging problems at the intersection of AI, robotics, and embodied intelligence.

We are seeking engineers to design and deploy tools for experiment lifecycle, inference fleet orchestration, and data pipelines, while owning end-to-end project delivery with CI/CD and observability.

Qualifications

• Bachelor's degree or equivalent hands-on experience in Computer Science, Engineering, or a related technical field.
• Proficiency in at least one scripting (Python or Bash) and one compiled (Java, Rust, or Go) language.
• Experience in shipping production-quality developer tools.
• Proficient in CI/CD automations (pipelines, runners, deployment).
• Experience in building reproducible pipelines end to end, and can explain which parts are bit-reproducible and why.
• Proven understanding of event-driven architecture, concurrency models, fault tolerance, and data consistency patterns or related experience in large-scale inference/serving infra.

Responsibilities

• Experiment Lifecycle Tracking and Tooling: design, build and deploy tools that own how a run is defined, launched, resumed, and killed.
• Inference Fleet Orchestration: design and operate the serving control plane for large volumes of concurrent requests, optimize throughput and latency.
• Model Evaluation in CI: design and operate automatic model checkpoint evaluation systems.
• Data Pipeline Operations: design and operate high-performance, fault-tolerant distributed backend services for large-scale data processing.
• Visualization Platforms: design and deploy observability and dataset visualization platforms with interactive data exploration.
• End-to-End Ownership: lead projects through specs, implementation, CI/CD, on-call support, and production observability.

Skills

Python

Bash

Go

Java

Rust

Concurrently/Scalable design

Event-driven architectures

Education

Bachelor's degree or equivalent hands-on experience in Computer Science, Engineering, or a related technical field

Tools

CI/CD automation

Argo Workflows

Ray

Slurm

Distributed serving infrastructure

Job description

About Us

Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.

About Us

Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.

Responsibilities

• Experiment Lifecycle Tracking and Tooling: Design, build and deploy tools that own how a run is defined, launched, resumed, and killed. Build and operate experiment databases for code version tracking, data version tracking, reproducibility, and checkpoint ancestry.
• Inference Fleet Orchestration: Design, build, and operate the serving control plane that accepts large volumes of concurrent client requests and assigns them across inference clusters. Develop cache-aware admission, routing, batching, and scheduling policies that improve cache locality, balance workload, protect tail latency, and keep the fleet highly utilized and reliable. Partner with ML Performance on model runtime, kernel, and per-worker throughput optimization.
• Model Evaluation in CI: Design, build, and operate automatic model checkpoint evaluation systems on seeded rollout and policy-success suites, run per-change and nightly.
• Data Pipeline Operations: Design, build, and operate high-performance, fault-tolerant, distributed backend services and event-driven systems for our large scale data processing pipeline.
• Visualization Platforms: Design, build, and deploy experiment observability and dataset visualization platform(s) that provides interactive data visualization, progress tracking, search, and comparison.
• End-to-End Ownership: Lead projects through the complete software lifecycle, including technical specs, implementation, CI/CD, on-call support, and production observability.

Requirements

• Bachelor's degree or equivalent hands-on experience in Computer Science, Engineering, or a related technical field.
• Proficiency in at least one scripting (Python or Bash) and one compiled (Java, Rust, or Go) languages.
• Experience in shipping production-quality developer tools.
• Proficient in CICD automations (pipelines, runners, deployment)
• Experience in building reproducible pipelines end to end, and can say precisely which parts of a training run are bit-reproducible, which are not, and why.
• One of the following:
  • Proven understanding of event-driven architecture, concurrency models, fault tolerance, and data consistency patterns.
  • Experience building or operating large-scale inference control planes or distributed serving infrastructure, with hands-on work in traffic management, admission control, request scheduling, routing, batching, or cache-aware load balancing; able to reason clearly about cache locality, queueing, tail latency, availability, and fleet utilization.
  • Experience working with multi-node workloads and building around slurm based scheduling systems.
  • Experience working on in-production model evaluation frameworks, in particular regression testing of large multimodal models.

NICE TO HAVE

• You have run experiment tracking at scale, logging video, 3D, and trajectory artifacts rather than only scalars.
• You have built evaluation harnesses for generative or embodied models, where quality is a distribution rather than a pass/fail.
• Full stack development experience with web-based front-end.
• You have orchestrated ML workflows with Argo Workflows, Flyte, or Ray, and know where each one breaks.
• You have built GPU-hour attribution that maps cluster spend back to specific experiments and teams.
• You have contributed to open-source ML tooling, or published on evaluation or reproducibility methodology.