Applied AI Engineer — Pointer

davidjoseph-co; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Competitive equity

Relocation support

Job summary

Pointer seeks an Applied AI Engineer in San Francisco to own the intelligence powering our browser automation and document understanding. You will push state-of-the-art automation across UI interaction, unstructured data, and tool use, turning research into production within a small, fast-moving team.

The role requires strong Python and PyTorch skills, a track record of shipping, and the ability to communicate clearly without buzzwords.

Qualifications

• Strong Python and ML frameworks experience, especially PyTorch.
• Applied ML/AI engineering background at a strong company.
• Metrics-driven mindset—focus on production-relevant evaluation.
• Comfort with messy data and turning it into usable signals.
• Track record of shipping end-to-end systems, not just research.
• Crisp, buzzword-free communication about what you built.

Responsibilities

• Own the intelligence powering Pointer's automation across browser agent reliability, document understanding, and inference optimization.
• Push core automation capabilities to state-of-the-art: UI interaction, unstructured data parsing, and tool use.
• Design fine-tuning pipelines learning from customer-specific workflows.
• Optimize latency across the stack via model selection, quantization, caching, and routing.
• Improve reliability and accuracy on real enterprise data.

Skills

Python

PyTorch

ML frameworks

Eval metrics

Shipping experience

Clear communication

Job description

Pointer — Applied AI Engineer

Type: Full-time | On-site (5 days/week) | San Francisco, CACompensation: $180,000–$250,000 + competitive equityHiring count: 1Visa sponsorship: Yes — H-1B, O-1Reports 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 operationsWebsite: pointer.aiOffice: 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, 2026Calibration anchors (applied ML/AI at a strong company) — Ramp, Databricks, Scale, StripeProfile types — Respected Series A–D startups and selective technical orgsResearch labs (paired with shipping) — SAIL, BAIR, MIT CSAIL, and similar