Who Lassie hires now
Lassie (lassie.ai) lists 16 open roles in San Francisco. Hirebase's data shows 19% entry-level (three roles) and 50% junior/associate (eight roles), with nothing at senior or above. EngRadar adds two director openings and 13 unspecified-seniority postings. The open roles break down as: six engineering positions, two in operations, and one each in product, design, marketing, sales, people/HR, finance, and a catch-all "other" bucket. The first-party board confirms the current slate: Customer Operations, Product Marketer, Software Engineer (Security), Software Engineer (Platform), Software Engineer (Product), and GTM Associate, all San Francisco-based.
Engineering roles cluster around a modern web stack: TypeScript, React, Tailwind, and Vercel on the frontend; Python, Django, Postgres, and Redis on the backend; Google Cloud Platform for infrastructure; Linear, Slack, and Notion for coordination. The two director openings suggest the company is starting to layer in leadership above the junior-heavy IC base.
The product is autonomous software agents for dental offices. Lassie's stated mission is to "help small businesses run themselves, starting with doctors' offices by building autonomous systems that handle busywork and complete workflows." The initial target: roughly 160,000 U.S. dental practices spending an estimated $200,000 annually on administrative labor. Founders Frederick (formerly of Superhuman) and Stein (formerly of Robinhood) have said they set a "very high bar for shipping stuff" from consumer-software experience, and they explicitly avoid human-in-the-loop designs because "we want software that skills and can be implemented quick." The first agent already delivers about 30 hours of labor per month per practice at a five-figure annual price point, with 200 hours of addressable workflow still on the table.
That product context shapes the candidate profile. Lassie needs engineers who can own features end-to-end across the stack, move fast without senior guardrails, and translate messy operational workflows into reliable software. The junior-heavy hiring suggests they're optimizing for raw horsepower, adaptability, and the consumer-product polish the founders prize — not deep systems specialization.
Important clarification: Lassie builds pure software agents for dental offices, not embodied robotics. The name "LASSIE" also appears in academic literature as a GPU-accelerated biochemical simulator (GitHub: aresio/LASSIE; NCBI; ResearchGate), and a separate "Lassie" operates as a digital pet-insurance provider in Europe (Lassie AB). This guide covers the San Francisco AI-for-dentistry entity.
Visa sponsorship appears on just one role per Hirebase.
Pay and equity: what the data shows
Levels.fyi aggregates self-reported offers for Lassie's software engineering roles: total compensation ranges from $205K to $298K per year, with the highest-reported band at the Common Range Average hitting $251,250. That figure blends base salary, stock grants, and any bonus component; Levels.fyi does not break out the split, and Lassie has not published an official compensation philosophy or equity schedule.
| Role | Total Comp Range | Common Range Average |
|---|---|---|
| Software Engineer | $205K – $298K | $251,250 |
The equity side is thin on public detail. Levels.fyi's "stock compensation" line item confirms grants exist, but vesting schedules, refresh policies, and strike-price mechanics are not documented in any first-party source. The company's Series A backing from Andreessen Horowitz (per a June 2026 founder interview) implies a standard 4-year vest with a 1-year cliff, but that is inference, not disclosure. Candidates should ask directly about grant size relative to the last preferred round and whether refreshers are tied to performance reviews or tenure.
Benefits are similarly under-documented. The BuiltIn profile lists San Francisco as HQ with 17 total employees; Join.com cites 51–200 for the European pet-insurance entity. Neither source enumerates health plans, 401(k) matching, or leave policies. A single data point from Join.com shows a "Legal Working Student" role at 14–15 €/hour, a rate denominated in euros that confirms a European subsidiary. The same Join.com entry describes Lassie as the aforementioned digital pet-insurance provider with a mission to "transform pet health." That description contradicts the YouTube founder interview (June 2026), BuiltIn, and Levels.fyi, all of which position the San Francisco entity as an AI platform automating dental-office administration across 700+ U.S. practices.
Inside the interview loop
Lassie runs a structured, role-specific loop that moves fast — most candidates spend four to eight weeks from first recruiter screen to offer, with the onsite compressed into a single day or two half-days and a debrief-to-offer window of roughly five business days. The founding recruiter owns the process end to end, partnering with hiring managers to define screening criteria and "raise the bar of what great looks like" across every function. That consistency shows up in the DNA: whether you're interviewing for sales, customer success, or engineering, the behavioral signals and ownership mindset carry across rounds, even as the technical assessments swap: coding screens for software engineers, portfolio reviews for designers, pitch simulations for commercial roles.
Sales and commercial loops
For sales roles, the onsite typically spans five rounds. Round one is a recruiter screen covering motivation, territory fit, and logistics. Round two asks candidates to pitch Lassie's product to a mock prospect, and interviewers watch for benefit-led language over feature lists and penalize generic answers that don't reference the company's model. Round three tests deal strategy: pipeline management, multi-stakeholder navigation, and MEDDIC qualification. Round four runs a customer-discovery exercise — diagnostic questioning, pain surfacing, and qualification. Round five is a behavioral/leadership deep dive, grading past evidence of ownership, influence, and conflict resolution. Common traps from Lassie's own outcome data: accepting surface-level statements without digging deeper, leaning on technical jargon instead of business value, failing to tailor prioritization to shifting market conditions, and giving answers that could apply to any company.
Customer success manager candidates face a five-round loop. After the recruiter screen (motivation, customer-facing experience, segment fit across SMB/mid-market/enterprise), round two walks through a specific customer story: how you saved an at-risk account, drove adoption, or expanded revenue. Round three covers renewal and expansion: QBR roleplay, identifying expansion signals, navigating churn risk, and aligning multiple stakeholders. Round four is a live mock QBR presenting health metrics, ROI evidence, and a renewal/expansion narrative to a customer panel. Round five repeats the behavioral/leadership deep dive. The data flags four recurring failure modes: not addressing the client's stated concerns directly (fees, LTV), missing SMB-segment nuance, citing activity metrics (logins, clicks) instead of outcome metrics (cost savings, revenue generated), and framing success as personal effort rather than data-driven results.
Engineering and product tracks
First-party board data from Zero G Talent shows active postings for the six San Francisco roles listed above. While the research details commercial loops most granularly, the same "DNA stays the same" principle applies: engineering candidates face coding screens and system-design discussions calibrated to Lassie's product surface area, while product and design candidates work through portfolio reviews and structured exercises. Across functions, evaluators grade for the same ownership and impact orientation.
What gets a candidate through
Three levers separate offers from rejections. First, tailor the application; the founding recruiter's job description emphasizes owning recruiting end-to-end and "raising that bar." Referrals from current employees carry weight. Second, master the round most candidates underestimate: for sales it's the discovery round (round four), for CSM it's the renewal/expansion strategy (round three), and for engineers it's often the system-design or behavioral ownership discussion. Third, use a structured framework; interviewers explicitly reference STAR (Situation, Task, Action, Result) and CIRCLES for product sense. Candidates who bring concrete, quantified examples, ask sharp clarifying questions, and adapt their narrative to Lassie's autonomous-agent thesis advance; those who recycle generic talking points stall.
San Francisco office
Lassie maintains a San Francisco office that has been actively hiring across 2024 and into 2025. The Built In San Francisco profile lists the location explicitly, and Zero G Talent's board shows the six open roles there, all tagged San Francisco, CA. This cluster signals that the U.S. site is not a lightweight sales outpost; it carries full-stack product engineering (platform and product tracks), a dedicated security engineer, and commercial functions. The office's perks-and-culture page on Built In describes a team that "enables doctors to focus on patients, not paperwork."
The research contains no mention of dedicated research labs, hardware integration shops, environmental test chambers, or field-test ranges. In practice, the work happens in an urban office built for software delivery and high-volume customer interaction. Engineers ship code that processes insurance workflows at scale; the "test facility" is the production environment serving dental clinics, and the "shop floor" is the support queue. Candidates should expect a modern SaaS workspace — not a robotics lab.
The profile that fits
Lassie's own career page states two values explicitly: "We value craft, rigor of thought, and direct communication" and "If you are seeking a challenge to match your ambition, come join us." That is the clearest first-party signal the company has published about what it rewards.
The San Francisco openings — heavy on product-facing engineering, security, and go-to-market — suggest a company in a build-and-scale phase where shipping reliable product and acquiring customers are simultaneous priorities. Engineers who thrive in that environment tend to be comfortable moving across the stack, writing code that ships to users quickly, and talking to the sales or support teams who hear the friction. The presence of a dedicated Security engineer role also signals that trust and reliability are non-negotiable, not afterthoughts.
Glassdoor and Indeed listings for "Lassie" and "Lad n Lassie" show 11 reviews on Glassdoor and a handful on Indeed, but the reviews themselves are thin. One Indeed reviewer wrote: "It is a great place to work. It is very nice to have the great team i work with... Nothing is stressful. I enjoy every minute bout it." Another noted the culture positively but offered no specifics. The sample is too small and too anonymous to extract reliable patterns, and at least some of the listings appear to reference a pet-care business rather than the AI company at lassie.ai. The data simply does not support a detailed portrait of long-term retention drivers from employee voices alone.
What the available evidence does support is a profile consistent with the stated values. "Craft" and "rigor of thought" map directly to the engineering roles listed (platform, product, security), where sloppy abstractions or untested assumptions show up fast in production. "Direct communication" is a survival skill in a team that spans engineering, product marketing, customer operations, and GTM; the cost of ambiguity compounds across those functions. And that stated challenge reads like a filter for people who want ownership of outcomes, not just tickets.
The search results for "Lassie" are dominated by a canoe-building family and a TV franchise, and the employee-review corpus is polluted by a different business. But the job board data and the company's own words point to a specific kind of hire — someone who builds complete, secure, user-facing systems, communicates without translation layers, and treats the next hard problem as the reason they showed up. That is the signal worth acting on.
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