Pointer — Applied AI Engineer

Type: Full-time | On-site (5 days/week) | San Francisco, CA Compensation: $180,000–$250,000 + competitive equity Hiring count: 1 Visa sponsorship: Yes — H-1B, O-1 Reports to: Not specified on role page

About Pointer

Pointer builds AI that operates computers the way humans do — navigating browsers, processing documents, and working through legacy systems — to automate the messiest enterprise finance operations. It is going after the $300B+ BPO industry built on labor arbitrage that software historically couldn't touch, because people were the product. Pointer recently raised a $6M seed round from Amplify Partners (first investors in Datadog, Modal, and other category-defining infrastructure companies). Early customers range from $500M to $5B in revenue, including a $2B property-management company automating accounts payable and invoice processing, and one of Belgium's largest retailers reconciling orders across decades-old legacy systems.

Founded: 2025 | Team size: 6 (4 full-time, 2 interns) | Total funding: $6M (Seed) Industry: Applied AI · enterprise automation · finance operations Website: pointer.ai Office: San Francisco, CA

Why Candidates Should Join

• Category-defining problem: Building AI that actually operates software end-to-end to attack a $300B+ market software couldn't previously touch.
• Top-tier backing: $6M seed from Amplify Partners, the first money into Datadog and Modal.
• Real enterprise traction: Live customers from $500M to $5B in revenue, including a $2B property manager and a major Belgian retailer.
• Frontier research-to-production work: Browser agent reliability, document understanding, fine-tuning pipelines, and inference optimization — shipping improvements every week.
• Ground-floor ownership: A six-person team in SF; this hire owns the intelligence layer that powers the whole product.

Intake Call Summary

• No intake-call transcript was supplied with this role page — an intake video is linked on Contrario but is not transcribed. Treat the points below as calibration signals surfaced on the page, not a verified intake summary.
• Calibration anchors for "strong company": Ramp, Databricks, Scale, and Stripe were named as reference points for the kind of applied-ML/AI background they want.
• Highest-signal background: Lab or research exposure (SAIL, BAIR, MIT CSAIL, similar) paired with evidence of shipping — the combination, not research alone.
• Roadmap adjacency matters: Recent work on LLMs, agents, RAG, fine-tuning, or production ML maps directly to Pointer's roadmap (browser agent reliability, document understanding, inference optimization).
• Communication bar: They explicitly screen for people who can describe what they built in a few clear sentences without buzzwords; script-like or keyword-stuffed self-presentation is a turn-off.

The Role

Own the intelligence that powers Pointer's automation. You'll turn research into production across browser agent reliability, document understanding, and inference optimization — making the system more accurate and faster every week.

What You'll Be Doing

• Push core automation capabilities to state-of-the-art: UI interaction, unstructured-data parsing, and tool use.
• Build adaptive systems that self-heal when environments change.
• Design fine-tuning pipelines that learn from customer-specific workflows.
• Optimize latency across the stack via model selection, quantization, caching, and routing strategies.
• Improve browser agent reliability and document-understanding accuracy on real enterprise data.

Tech stack: Python, PyTorch, and modern ML frameworks; LLMs, agents, RAG, and fine-tuning; inference optimization (quantization, caching, routing).

Requirements

• Strong Python and ML frameworks, particularly PyTorch.
• Applied ML/AI engineering experience at a strong company.
• Eval-and-metric mindset — thinks in terms of metrics that matter in production, not just benchmarks.
• Comfort with messy data and figuring out how to make it useful.
• Track record of shipping — can describe specific systems built end-to-end, not just research.
• Crisp communication about own work — can describe what they built in a few clear sentences without buzzwords.
• Based in San Francisco or willing to relocate; in-person 5 days a week.
• Recent, recognizable high-caliber pedigree — current or recent experience at a marquee/recognized company: FAANG-caliber, a top AI lab, or a well-known Series A–D startup (Ramp, Scale, Databricks, and Stripe are the calibration anchors). Seed-stage or unknown startups, university research labs, IT-services firms, large non-tech enterprises, and government/federal roles do not clear this bar. [Added July 29, 2026]
• Core applied-AI/LLM work, not adjacent-domain — the applied-AI experience must be genuine research/applied-AI on LLMs (agents, RAG, fine-tuning, inference optimization, evals). Chip, embedded, hardware, edge, or data-engineering-adjacent work does not count, even at a marquee company. [Added July 29, 2026]
• Clear marker of excellence — a recognizable signal of a high talent bar, such as having worked at a prestigious company or holding a degree from a prestigious institution (or a comparable standout credential). Strong work without such a marker tends to get screened out. [Added July 29, 2026]

Green Flags

• Real applied ML or AI engineering work at a respected Series A–D startup or selective technical org (calibration anchors: Ramp, Databricks, Scale, Stripe).
• Lab or research exposure (SAIL, BAIR, MIT CSAIL, or similar) paired with evidence of shipping, not just publishing — the combination is the highest-signal background.
• Recent momentum toward LLMs, agents, RAG, fine-tuning, or production ML systems; direct adjacency to Pointer's roadmap (browser agents, document understanding, inference optimization).
• Experience with RL, retrieval systems, or agent-based systems.
• Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring.
• Published ML papers or significant OSS contributions.

Red Flags

• Resumes or LinkedIn profiles stuffed with 300–400 word descriptions full of buzzwords and keywords.
• Inability to clearly articulate what they actually built and how they thought through problems.
• Communication style that sounds like reading off a script or cue card.

Role Details

• Salary — $180,000–$250,000
• Equity — Competitive equity
• On-site policy — In-person in SF, 5 days a week (relocation supported)
• Visa sponsorship — H-1B, O-1
• Employment type — Full-time
• Location — San Francisco, CA
• Experience band (per role page) — 0–4 years

Screening Questions

• None specified on the role page — confirm with Contrario / the hiring manager before screening calls.

Interview Process

• Stage 1 — Initial conversation — Behavioral chat focused on how you think, what you're interested in, and general fit.
• Stage 2 — Technical deep dive — Conversation about what you've built and how you think through problems (not whiteboarding or leetcode; the focus is walking through your actual work).
• Stage 3 — Take-home assessment
• Stage 4 — On-site work trial (1–2 days) — Working alongside the team on real problems. Pointer covers flights, accommodation, and compensates for your time.
• Stage 5 — Offer Extended
• Stage 6 — Candidate Hired — Candidate accepts and starts.

(Benefits & perks: coffee/lunch/dinner/snacks covered, M4 Pro/Max MacBook Pro + 2+ monitors, unlimited PTO, 401(k).)

Ideal Companies & Backgrounds

Updated June 24, 2026 Calibration anchors (applied ML/AI at a strong company) — Ramp, Databricks, Scale, Stripe Profile types — Respected Series A–D startups and selective technical orgs Research labs (paired with shipping) — SAIL, BAIR, MIT CSAIL, and similar