Careers at Nori: Teams, Pay and How to Get Hired

The Team and What It Pays

Nori Robotics, a Y Combinator-backed robotics startup building a two-armed household robot called the A3 (priced at $1,688), has posted ten salaried roles in San Francisco with a compensation band stretching from $48,000 to $250,000 (median $150,000). Founder Antonio Sitong Li runs an engineering-first operation. The salary spread is wide by design; the company hires across a spectrum from hardware technicians to foundation-model researchers, and the bands reflect the market for each discipline rather than a single internal grade.

The three research roles — VLM Researcher, LLM Researcher, and Applied AI Engineer, sit at the ceiling ($150K–$250K), reflecting the premium for candidates who have shipped production multimodal systems or contributed to open foundation models. Hardware-adjacent roles cluster at the lower end. Full-Stack Engineer, Data Operations Lead, and Design Engineer share a middle tier ($100K–$150K).

The board postings do not disclose strike prices, pool size, or vesting details. Relocation assistance is listed on the company page.

The team splits across three functional clusters. At the hardware–automation layer sits the Robot Assembly & Automation Technician, the only sub-$100K slot and the only position reading like a traditional bench-tech hire. Above it, the Electrical Engineer and Design Engineer form the core of the electromechanical group, suggesting a product that needs custom PCBs, actuator integration, and mechanical design tight enough to ship from a single San Francisco site. The software stack fans into two tracks: Full-Stack Engineer and Data Operations Lead handle data pipelines, fleet telemetry, and internal tooling; the research-heavy tier carries the highest bands. Model development, vision-language fusion, and agent architectures are not side projects — they are the product. A single Social Media & Content Marketer at $35/hr rounds out the board, likely tasked with translating deep-tech output for recruiting and investor audiences.

How the Hiring Process Works

Nori Robotics is a compact team. The Zero G Talent board shows ten open roles; the New Atlas profile describes a startup assembling 100 A3 units in its San Francisco facility this fall. With no dedicated recruiter listed, applications likely reach the founding team directly. Roles at this stage typically attract dozens of applicants within weeks. Candidates who move fast and bypass the standard portal (cold outreach to the founding team) get seen first.

The screen weights relevant experience and demonstrated ability to operate autonomously. A generic CV gets rejected quickly; the team notices when a resume hasn't been tailored to the specific role. Matching the job description's language helps clear any automated filters, but the real filter is human: Li or a senior engineer scanning for evidence that you've owned outcomes in environments that look like Nori's: robotics hardware, multimodal models, or large-scale data pipelines.

Stage one is typically a 30–45 minute screen with a founder or senior engineer. Interviewers listen for self-awareness paired with a concrete mitigation, not a humblebrag.

Stage two is a technical deep-dive. For engineering roles (Full-Stack Engineer, Electrical Engineer, Applied AI Engineer, VLM Researcher, LLM Researcher), this means live coding, system design, or a research walkthrough of a recent paper or project. For Data Operations Lead or Design Engineer, expect a practical exercise tied to Nori's actual data or hardware constraints. The evaluation weights technical proof over pedigree; a candidate who can articulate trade-offs in a messy real-world deployment beats one with a brand-name resume but shallow ownership.

Stage three is a team conversation, often a group call with the engineers you'd work alongside. This is where autonomy signals get stress-tested. Pulling a specific line from the job description and asking a clarifying question shows you read it and you're thinking in context.

Most applications at sub-50-person companies get ghosted. A day-five follow-up (polite, specific, referencing something from the conversation) can double response rates. Hiring managers at this stage often decide within the first 15 minutes whether a candidate advances; the rest of the interview confirms or overturns that instinct. The disqualifiers are consistent: generic materials, inability to articulate ownership, and questions that reveal zero research on Nori's product or technical stack. The candidates who clear the process treat the application as a technical deliverable — customized, concise, and built for the reviewer's constraints.

Where the Work Happens

Every role on the board lists the same location: San Francisco, CA. The A3 — a wheeled two-armed household robot that uses LiDAR and cameras to navigate homes, runs up to eight hours on a charge, and ships at a $1,688 price point, dictates the kind of physical space a team like this needs. The role mix makes it explicit: a Robot Assembly & Automation Technician at $48K–$58K, an Electrical Engineer at $110K–$120K, and a Design Engineer at $100K–$130K signal a hardware lab with bench stations, test rigs, and enough floor space to iterate on a mobile manipulator. Meanwhile, the two roles at $100K–$150K each, a Multimodal Engineer at $130K–$200K, an Applied AI Engineer at $150K–$200K, and both a VLM Researcher and LLM Researcher at $150K–$250K each point to a compute-heavy research environment (GPU clusters, data pipelines, and simulation infrastructure) sitting alongside the hardware lab.

San Francisco's office market in mid-2026 reflects the pressure this kind of team faces. Citywide vacancy fell five points year-over-year to 29.7% in Q2 2026, driven largely by AI-company expansion. AI firms have accounted for 30% of all leasing activity since 2023. Transit-adjacent microneighborhoods like Transbay saw availability drop to 12.4% per CBRE data. Nori's listing doesn't name a specific address, but a hardware-plus-AI team of this size typically clusters near SOMA, Mission Bay, or the Financial District, close to suppliers, contract manufacturers, and the talent pool graduating from Stanford and Berkeley. The city's PermitSF reforms have cut permit timelines from months to weeks for some applicants, a practical advantage when a robotics lab needs build-out for power, ventilation, and ESD-safe zones.

The research doesn't disclose Nori's exact footprint: square footage, lease terms, or whether they occupy a dedicated lab, a shared hardware incubator, or a hybrid office-lab suite. What the role mix makes clear is that the space must support both hardware bring-up (assembly, electrical validation, mechanical tolerance testing) and the compute demands of training and deploying foundation models on robot hardware. That role suggests a public-facing component, likely documenting the A3's development for the "built for people who want to actually build with one" community Nori courts.

Li leads a team that, per the board, totals roughly ten salaried roles. That headcount fits a single cohesive site rather than a distributed footprint. In a market where AI leasing has tightened the best blocks, a compact engineering-led team stays in one room — hardware and research talking across desks, not Slack, because the A3's differentiation lives at the intersection of manipulation hardware and the models that drive it.

Who Thrives Here

The Zero G Talent board data describes a San Francisco robotics company posting ten salaried roles with a $48K–$250K band and founder Antonio Sitong Li attached, a compact, engineering-led, research-heavy frontier-tech signal. The role slate is top-heavy with model work: vision-language, large language, agentic robot learning. The design and electrical engineers at the bands noted above sit alongside them, suggesting the team still touches hardware, but the compensation weight sits in the model layer. The board's median of $150K and ceiling of $250K for research roles implies the company pays for demonstrated technical ownership in the specific domain the role requires.

The A3 sits in the lab, waiting for the models that will make it useful. The next hire writes them.

The Board Is the Signal

Li reviews every application himself. The band stretches from $48K to $250K. The team fits in one room. A candidate who ships a tailored resume, articulates ownership in messy deployments, and asks questions that prove they've read the spec — that candidate gets a call. The rest get ghosted.

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