Senior/Staff Software Engineer, Nuro
AI Agent Infrastructure
$193.9-352.3k
This position is also eligible for an annual performance bonus & equity
Office located in Mountain View, CA
Autonomous delivery vehicle manufacturer
Open for applications
Autonomous delivery vehicle manufacturer
1001+ employees
Open for applications
$193.9-352.3k
This position is also eligible for an annual performance bonus & equity
Office located in Mountain View, CA
1001+ employees
Company mission
To better everyday life through robotics.
Role
Who you are
- 5+ years of software engineering experience (or 4+ with a Master's) in computer science, engineering, or equivalent practical experience. Staff-level candidates should bring correspondingly deeper scope and ownership
- Deep, current taste in LLM research. You understand how a model is trained from scratch — data, tokenization, architecture, pretraining dynamics, the full post-training stack of supervised fine-tuning, preference optimization, and RL — and you can reason about what a training decision does to model behavior. You follow the literature because you want to, not because it is on a roadmap
- You know what happens under the hood at inference. Attention and KV-cache behavior, batching and scheduling, quantization, speculative decoding, prefix caching, context handling, and how each trades off latency, throughput, and cost
- You have built and operated LLM-based agent systems in production — tool use, orchestration, sandboxing, retrieval, memory — and you know where they break
- Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up
- Strong programming skills in Python
- You are opinionated about evaluation. You have argued with someone about whether a benchmark measured anything real, and you were right
- You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification
- Hands-on post-training or fine-tuning experience — SFT, preference optimization, RL, distillation — including the evaluation work required to know whether it helped
- Experience with ML training or research infrastructure: experiment orchestration, evaluation pipelines, hyperparameter search, data pipelines
- Experience running inference serving, cost, or capacity at meaningful scale
- Familiarity with agent architecture patterns: planning, reflection, long-horizon memory, multi-agent coordination
- Experience with open tool-integration protocols, plugin or skill frameworks, and model-routing or gateway layers
- Background in developer experience or platform engineering, observability, or security isolation
- Go, C++, or Rust experience in addition to Python
What the job involves
- We already operate a substantial agent system in production — a fleet of agents with an extensive library of skills and plugins, integrated into the tools our engineers use daily
- This role is about what it takes to make that system trustworthy, autonomous, and an order of magnitude more capable
- Three things sit at the center of it
- Closed-loop evaluation: Our ambition is to build the most rigorous evaluation system for AI work anywhere — closed-loop, meaning every agent action produces a measurable outcome that feeds back into whether that agent is trusted to act again
- Acceptance, revert, and override rates per workflow
- Statistical honesty about whether a difference is real
- Regression detection that fires before a human notices
- Everything else on this team depends on this being right, and almost nobody has built it well
- Agent platform: The runtime that makes autonomous agents safe to run against real systems: orchestration, sandboxing and isolation, tool and skill frameworks, memory, identity and permissioning, and the gateway and observability layer underneath
- Agents that touch production code and production infrastructure need containment and auditability before they need capability
- Autoresearch infrastructure: The automation of the research loop itself: agents that read the current state of a model and its metrics, form a hypothesis, launch an experiment, evaluate the result honestly, and either propose a change or discard the idea and move on
- At Nuro that loop runs against the training pipelines behind the driving model — real experiments, real compute budgets, real metrics that determine whether a behavior ships
- The hard parts are trusting the measurement, surviving experiments that take days, spending finite research compute wisely, and producing proposals a skeptical researcher can audit and reject
- Alongside this, the team builds agent-powered tooling across the engineering lifecycle — code generation, review, debugging, test and CI failure attribution, knowledge retrieval, triage
- There is also appetite on this team for post-training our own models where an internal workload justifies it, and the engineer in this role would be central to that work
- Build the closed-loop measurement layer that tells us, per workflow, whether agent output is accepted, reverted, or overridden — and use it to decide where autonomy expands and where it gets pulled back
- Take the autoresearch loop from assisted to unattended for a bounded class of experiments, including the eval and confidence machinery required to run it without a human in the loop
- Design the isolation and permissioning model that lets agents act on production repositories and infrastructure with an auditable record of what they did and why
Our take
Nuro offers custom-built autonomous vehicles for the delivery of physical goods, rather than people. Its vehicles have a distinctive, futuristic, design that has not only made the vehicles noticeable from a marketing point of view, but, has also made its vehicles stand out to other road users, improving safety.
Nuro is an exciting addition to the autonomous vehicle landscape. Without a passenger inside the vehicle, it has been able to sidestep some of the safety issues that have plagued other autonomous vehicle brands. Additionally, the company has landed several high-profile partners, including Uber, Walmart, Chipotle, and Kroger.
With home deliveries at an all-time high, many companies are looking for ways to get goods to customers’ homes as fast as possible. Low-cost, efficient services such as Nuro's offering are becoming increasingly attractive. Following Series E funding in 2025, the company is focusing on growing new commercial partnerships and scaling its technology.
Steph
Company Specialist at Welcome to the Jungle
Insights
-29% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Apr 2025
$203m
SERIES E
Nov 2021
$600m
SERIES D
Total funding: $2.3bn
Company benefits
- Free Caltrain pass and commuter benefits
- Company stock options
- Work from home opportunities
- Health insurance
Company HQ
Old Mountain View, Mountain View, CA
Leadership
Jiajun Zhu
(Co-Founder & Co-CEO)After an MA in Computer Science at the University of Virginia, Jiajun worked as Principal Software Engineer at Google for over 8 years. He left this role in August 2016 to co-found Nuro with Dave.
Dave Ferguson
(Co-Founder)Experienced Robotics Engineer with stints at Google (Computer Vision and Machine Learning Lead) and Intel (Senior Research Scientist). PhD in Robotics from Carnegie Mellon University.
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