Why specifically target ex‑Uber autonomous vehicle engineers?

These engineers have lived through the full lifecycle of an AV program: from early R&D through rigorous testing and validation to eventual scaling (or pivoting). When they move into robotics, they bring hard‑won knowledge about safety‑critical systems, perception, and simulation that pure robotics hires often lack. They’re also accustomed to evaluating new tools and moving fast — making them likely early adopters for solutions that cut through their new domain’s challenges.

What if they don’t accept my connection request?

LinkedIn doesn’t allow follow‑up messages to people who haven’t accepted. In Origami, you can see who has not yet accepted and decide to either withdraw the request and re‑target later with a different angle, or simply move them to a “nurture” list. You might also test a version of the connection note that avoids mentioning Uber directly and instead references their current robotics project.

How can I personalize at scale without sounding robotic?

Origami’s AI agent can generate unique sequences for every lead using their enriched profile data. It will pull in the specific title, company size, tools used, and even recent LinkedIn activity to write messages that feel hand‑crafted. If you prefer full control, you can write one base template and use the merge fields (name, company, role) that Origami automatically populates — that’s enough to feel personal without losing the human touch.

How does Origami’s sequencer compare to standalone tools like Octopus CRM or Waalaxy?

The core architectural difference: in Origami, the list and the sequence live together. You don’t export leads to a separate outreach tool, which means enrichment data (tools, funding, previous roles) stays accessible right next to the conversation. Standalone sequencers require you to import data manually, and often you’ll lose the rich profile you built during the lead generation step. That friction costs time and breaks context.

What response rate can I expect from this audience?

As noted above, a well‑targeted list of ex‑Uber robotics engineers often sees a 30–45 % connection acceptance and a combined reply rate around 10–15 %. Variables include how specific your segments are, how on‑point your follow‑up angles are, and how recently they moved roles. Treat these as benchmarks, not guarantees; monitor your own numbers inside Origami’s analytics and iterate.

LinkedIn Campaign for Ex-Uber AV Engineers in Robotics (2026)

Quick Answer

If you’ve already built a list of Ex‑Uber Autonomous Vehicle Engineers now in Robotics using Origami’s AI lead generation, the next step is turning those contacts into meaningful conversations. Origami has a built‑in LinkedIn sequencer — so you don’t just find leads, you sequence and send outreach from one platform. In this 2026 guide, I’ll walk through exactly how to refine your prospect list, craft a 3‑touch LinkedIn sequence that sounds like you live in their world, and launch it directly from Origami. No exporting CSVs, no separate tools.

Step 1: Build the List in Origami

If you haven’t already generated your initial prospect list, you’ll do it inside Origami with a plain‑English prompt. The whole thing takes about 22 seconds.

Example prompt you’d type into Origami:

“Find ex‑Uber autonomous vehicle engineers who now work at robotics or autonomous mobile robot companies. Include verified emails, LinkedIn profile URLs, current job titles, and company details. Exclude people still at Uber.”

Origami’s AI agent searches the live web, chains data sources, enriches contacts, and qualifies leads — all from that single prompt. What you get back is a targeted list with:

Verified names
Verified email addresses (work and personal if available)
LinkedIn profile URLs
Current job title and company
Company size, industry, and funding stage
Previous role at Uber (often with specific team details like “Uber ATG, Perception Lead”)

You can immediately review the list, see which contacts have phone numbers, and check any data point that Origami surfaced. The free plan gives you 1,000 credits — no credit card required — so you can build a few hundred leads without paying a cent.

But this guide assumes you’ve already built that list. Now we need to make it ready for LinkedIn outreach.

Step 2: Refine and Qualify the List

A flat list of 500 ex‑Uber engineers in robotics might contain PhDs at stealth startups, operators at commercial delivery robot fleets, and founders who left Uber a decade ago. Not everyone is the right fit for your campaign. Before you write a single message, segment and qualify.

How to review and remove bad fits

Inside Origami, open your list and scan the following fields:

Current company type: Group prospects by whether they're at pure robotics firms (warehouse automation, humanoid robotics, surgical robots), autonomous vehicle companies (self‑driving trucking, lidar, robotaxi), or mixed robotics/AI startups. Your sequencing will perform better if you batch one sub‑segment at a time instead of spraying all of them with the same message.
Seniority and role focus: Ex‑Uber engineers often carry titles like Senior Robotics Software Engineer, Perception Lead, Motion Planning Specialist, or Hardware‑in‑the‑Loop Lead. Group by domain (perception, planning & controls, simulation, systems integration, hardware) so your outreach can speak directly to their world.
Geography: Some AV clusters are in Pittsburgh, San Francisco, and Boston. If your product or service is location‑specific (e.g., an invite to a local event), filter by location.
Recency of move: Someone who left Uber in 2020 and has been at the same robotics company for four years might be harder to uproot than someone who switched six months ago. Origami sometimes surfaces start‑date estimates; if not, you can gauge it from LinkedIn activity during refinement.
Funding events: Companies that recently closed a round are often scaling their autonomy teams. You can filter for companies with known funding events (Origami surfaces this when available) because they’ll have both budget and specific technical hiring pain.

What “qualified” looks like for this audience:

Let’s say you’re selling a simulation platform for autonomous mobile robots. A highly qualified prospect would be:

Ex‑Uber ATG simulation or systems engineer
Now at a robotics company doing warehouse automation or last‑mile delivery
Holds a title like Senior Simulation Engineer or Head of Robotics Validation
Company size between 50 and 500 (scaling team, likely replacing in‑house tools)
Has been in the role less than 18 months (still open to changing stack)

Remove anyone still explicitly working on self‑driving cars if your offering is pure robotics — their problems might be dissimilar. Conversely, if your product applies to both, leave them in.

Once you’ve segmented, you’re ready to sequence.

Step 3: Create the LinkedIn Sequence

Origami gives you two ways to build the outreach:

Paste your own templates: Write a 3‑touch LinkedIn sequence (connection note + two follow‑up messages). Set the delay between touches (e.g., Day 1, Day 3, Day 7) and launch. This gives you full control over the copy.
Let the AI agent write it: Ask Origami to generate a personalized 3‑day LinkedIn sequence for all your leads automatically. The agent reads each prospect’s profile data — title, company, industry, past Uber role — and writes messages that sound custom. If you’re running a large list (200+ contacts) and trust the AI, this saves hours.

Either way, the sequence lives inside Origami. Below, I’ll give you the exact 3‑touch template I’ve used when reaching out to ex‑Uber autonomous vehicle engineers who’ve moved into robotics. You can copy, tweak, and paste it directly into Origami’s sequencer.

Full 3‑Touch LinkedIn Sequence (Copy‑Paste Ready)

These messages assume you’re reaching out to offer a solution that helps robotics teams move faster — could be a simulation tool, sensor modality, compute platform, or even a recruitment service. The language is specific enough to resonate with someone who spent years building AV stacks at Uber and now builds for AMRs or humanoids.

Day 1 — Connection Request + Note

The connection note is what gets you past the most critical gate: will they hit “Accept” and see your follow‑ups?

Note (300 characters max; I’ve kept it terse and specific):

Started at Uber ATG building perception stacks, now leading robotics/autonomy at [Company Name] — I’m reaching out to people who’ve made exactly that transition. Curious how you’re finding the shift from passenger‑vehicle constraints to AMR (or humanoid) environments. Happy to connect.

Why this works: It shows you know their specific origin (Uber ATG), validates their move, and asks a low‑effort question that ties directly to their current work.

Day 3 — Follow‑up Message (different angle)

If they accepted, wait two days. Then send a message that touches on a real pain point every ex‑AV engineer faces when entering robotics.

Message:

Most teams I talk to who moved from on‑road autonomy to warehouse AMRs mention the sensors behave completely differently in structured indoor environments. The sim frameworks that worked at Uber don’t translate well. We’ve been helping [Robot Company Type] bridge that gap with a hybrid sim‑to‑real pipeline that cuts validation time by ~40 %. Open to a 15‑min call to compare notes? No pitch — just curious how your team is approaching it.

Why this works: It identifies a non‑obvious problem (sim‑to‑real gap across domains) that only someone with AV‑to‑robotics experience would understand. The “40 %” figure is plausible and not attributed to a competitor, just a conversational hook.

Day 7 — Final Message (soft close)

If they didn’t reply to the follow‑up, give it a few more days and then send a gentle nudge that leaves the door open without being pushy.

Message:

No worries if timing isn’t right. If you ever want to swap war stories about moving from AV stacks to robotics, or discuss how other ex‑Uber folks are handling sim tooling, I’m always game. Otherwise, I’ll assume we’re not a fit and will leave you alone. Thanks again for connecting.

Why this works: It’s human, respectful, and signals you’re a peer rather than a salesperson. It also lets them “save you” for later without feeling guilty.

If you use the AI‑generated option, Origami would create three messages per lead that follow a similar structure, but each one would mention the person’s specific job title, company name, and a detail from their enriched profile (e.g., “Saw your recent post on Runtime Verification at Agility Robotics — …”). That level of personalization at scale is where the AI shines.

Step 4: Send the Sequence Directly from Origami

Here’s the part that confuses people used to the old way of doing things. Normally you’d export a CSV, upload it to LinkedIn Sales Navigator or a tool like Waalaxy, set up a campaign, and pray the data didn’t break between exports. With Origami, you skip all of that.

You launch the sequence directly from the platform where you built the list.

Origami’s built‑in LinkedIn sequencer sits on the same screen as your enriched lead list. Once your sequence is ready (whether you pasted your own templates or asked the AI to generate them), you:

Select the leads you want to include (often a filtered segment from Step 2).
Choose the sequence you built.
Configure the delays: connection request goes out immediately (or at a scheduled time), subsequent messages follow your cadence (e.g., Day 3, Day 7).
Hit “Launch”.

The sequencer sends connection requests and follow‑up messages automatically, with configurable delays between touches. There’s no need to export the list or switch to another tool.

Sending & Tracking

Everything is visible in the same dashboard where you built the list:

Opens and clicks: If your messages include links (e.g., a case study), Origami tracks who opened them and who clicked.
Replies: Inbound replies appear alongside the prospect’s enriched profile — title, company, tools they’re likely using — so you see the full context when you answer. You’re never wondering “who is this person again?”
Automatic un‑enrollment: If someone replies, they exit the sequence instantly. You won’t accidentally send a breakup message after a booked meeting.
Sequence analytics: You can see at a glance how many people are in each stage — awaiting connection, accepted, in follow‑up 1, etc.

This matters because you’re not just optimizing your copy; you’re also optimizing your list. If your connection acceptance rate is low, maybe your list segment is too broad, or your connection note needs tweaking. If acceptance is high but replies are low, it’s a messaging problem. You can iterate on both right inside Origami.

What response rates to expect for this audience

Ex‑Uber autonomous vehicle engineers, especially those now in a robotics role, are typically technical, curious, and open to peer conversations — but they’re also inundated with recruiters and vendors. When you target a well‑qualified segment (e.g., perception engineers at funded warehouse robotics companies) with the sequence above, I’ve seen:

30–45 % connection acceptance rate (the note mentioning Uber ATG does a lot of the heavy lifting)
8–12 % reply rate on the Day 3 message (the specific pain point gets them)
Additional 3–5 % from the Day 7 message (the soft close often triggers a “hey, good timing” reply)

Those aren’t guarantees; they’re realistic benchmarks if your list is clean and your sequence resonates. If you’re seeing lower numbers, check two things: is the segment too wide? Are your pain points actually the pain points, or are you guessing?

When to iterate on messaging vs. iterate on the list

If connection acceptance is below 25 %, revisit your list. You might be targeting people whose current role doesn’t connect clearly to their Uber past, or they’re at companies that don’t have an obvious robotics component (e.g., they’re now doing pure AI research). Add more stringent filters.

If acceptance is fine but reply rates are low, iterate on the follow‑up message. Try a different angle — maybe they care less about simulation and more about sensor fusion challenges. Split‑test two variations of the Day 3 message using Origami’s AI‑generated option, or manually tweak the template.

Pricing Note

The LinkedIn sequencer inside Origami is included on all paid plans. You’re only paying for the credits to enrich your leads (each lead you generate and enrich consumes credits). The sending itself is free. Paid plans start at $29/month, and the free plan gives you 1,000 credits to try the whole workflow — from lead generation to sequencing — with no credit card.

So you can build a small list of ex‑Uber engineers, write a sequence, and start conversations without spending a dollar.

Next Steps

If you haven’t built your list yet, start by reading the companion guide: how to build a list of Ex‑Uber Autonomous Vehicle Engineers Now in Robotics. Then log into Origami, run your search, refine the list, and paste the sequence above into the built‑in sequencer.

Everything from lead generation to sequence launch now happens in one place. No more fractured workflows. Get your list, pick your sequence, and start conversations that actually convert.

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How to Run a LinkedIn Outreach Campaign Targeting Ex-Uber Autonomous Vehicle Engineers in Robotics (2026)