Applied AI Engineer - Pointer

About the job Applied AI Engineer - Pointer

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. 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-$250,000EquityCompetitive equityOn-site policyIn-person in SF, 5 days a week (relocation supported)Visa sponsorshipH-1B, O-1Employment typeFull-timeLocationSan Francisco, CAExperience 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 assessmentStage 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 ExtendedStage 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

No "Show all X companies" list was present on the page; the above is drawn from the named calibration anchors.

Ideal Candidate Profiles

For reference only - do not source these specific profiles. The role page flags these as DO NOT CONTACT.Pulkit Arya - LinkedIn URL not captured in HTML (icon button had no extractable link)Mohammed Tibian Zaman - LinkedIn URL not captured in HTML (icon button had no extractable link)

Rejected Candidate Feedback

None provided on the role page.