Senior / Staff ML Ops Engineer

Waabi is an artificial intelligence (AI) company that commercializes driverless trucks. Waabi is bringing the promise of self-driving closer to commercialization than ever before, thanks to its innovative approach that…

Location: Remote US & Canada
Company size: 10–200
Posted: Yesterday
Via: Lever

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Job listing · Updated 23 SEP 2026

Senior / Staff ML Ops Engineer

Waabi· Remote US & CanadaView company profile

Job title: Senior / Staff ML Ops Engineer
Job location: Remote US & Canada
Job listing URL: https://jobs.lever.co/waabi/1c01bf84-38e3-42db-990c-29c9195443ba
Job description: Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

You will..

Build and evolve our training infrastructure on Kubernetes with Infrastructure — GPU scheduling, autoscaling, multi-node distributed jobs, capacity strategy, and the operators and workflow engines that keep long-running training reliable.

Shape the developer-facing surface — CLIs, SDKs, job submission, templates, paved paths — designed with the teams who'll use them. Make the common case one command and keep the uncommon case possible.

Shorten the inner loop. Time to first training run, edit-to-signal latency, local iteration before a job hits the cluster, fast failure over slow mystery. Measure it, publish it, drive it down.

Evangelize best-in-class tooling and frameworks. Track what the ecosystem is shipping, evaluate honestly, and make the case with working prototypes and migration paths — or say plainly when a shiny thing isn't worth the switching cost.

Strengthen the data and artifact layer. Dataset versioning, sharding, and high-throughput loading of large multimodal sensor data, so jobs saturate GPUs instead of waiting on I/O.

Turn one-off Python into durable tooling — tested, documented, observable libraries, CLIs, and services with sane defaults, and deletions where they're overdue.

Make experiments legible, with the teams who live in them: experiment hygiene, dashboards researchers trust, a real model registry, and lineage from dataset to checkpoint to simulation result.

Ship CI/CD for models alongside autonomy and simulation, so a model change is validated the same way a code change is.

Build observability across the ML stack — utilization, throughput, failure modes, queue times, cost per experiment. When a job fails at 3am on node 47, the researcher should find out why without you.

Treat docs, onboarding, and support as product surface — golden-path guides, a new researcher productive on day two, office hours that turn repeat questions into shipped fixes.

Drive adoption, not just availability. Prototype with real users, watch them work, iterate. A tool nobody adopts didn't ship.

Make the platform boringly reliable — fewer failures, faster recovery, and none of the manual steps that quietly cost a team days.

Build guardrails that don't feel like walls, with Security, IT, and Infrastructure: access controls, data handling, and cost governance that hold up in an IP-sensitive environment while staying self-serve.

Qualifications:

5+ years of software or infrastructure engineering, including tools or platforms used by other engineers and operating ML or data-intensive production systems.

Hands-on Kubernetes expertise — GPU scheduling, autoscaling, Helm or equivalent, networking fundamentals, and the ability to debug a cluster under load rather than restart it.

Excellent Python, and a track record of designing APIs and CLIs other people enjoy using.
Practical AWS depth: object storage at scale, IAM, GPU compute, networking, cost management, and infrastructure as code (Terraform, Pulumi, or similar).

Distributed training in PyTorch (DDP, FSDP, or similar), plus experiment tracking and model registry tooling — from the perspective of someone who made them pleasant for others to use.

Fluency with containers, CI/CD, and modern build systems, including large monorepos.

The ability to influence without authority: evaluate a framework on its merits, pilot it credibly, and persuade skeptical senior engineers to change how they work.

A collaborative default — you'd rather co-own a system than draw a boundary around your part of it.

User empathy: you'd rather fix the third-most-interesting problem blocking ten people than the most interesting one blocking nobody.

Strong product instincts, strong writing, and comfort operating autonomously in ambiguous territory.

Passionate about self-driving technologies and frontier AI, and about what a small, world-class team can do with the right infrastructure.

Bonus/nice to have:

Internal developer platform, research platform, or DevEx work — with a story about a tool whose adoption you grew from zero.

Large-scale distributed GPU training: hundreds to thousands of accelerators, NCCL, high-performance cluster networking, collective communication tuning.

High-throughput loading of LiDAR or camera data, and formats such as Parquet or WebDataset.

Workflow and scheduling systems — Argo Workflows, Ray, Flyte, Kubeflow, or Slurm.

Build-system depth (Bazel or similar), including remote caching in a monorepo.

Simulation infrastructure or large-scale batch evaluation pipelines.

Background in ML, robotics, or autonomous systems infrastructure.

Security- and IP-sensitive production environments.

Open-source contributions to ML infrastructure or developer tools.