Applied AI Research Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Series B From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Applied AI Research Engineer San Francisco, California, United States Netic is the AI revenue engine for essential services who are the backbone of the American economy. With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America. There are now companies operating entirely AI-first on Netic. You'll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible. As an Applied AI Research Engineer, you'll dive deep into cutting-edge research, understand the business functions we put on autopilot inside-out, and execute targeted ML projects that deliver pure magic. What You'll Do: - Study the frontier: Track frontier work in traditional ML, LLMs, multimodal models, retrieval, and agentic systems-then distill it into ideas we can ship. - Identify high‑ROI projects: Partner with GTM and ops teams to spot bottlenecks in products; define ML projects that unlock significant leverage for customers. - Build targeted models: Own the full cycle-data curation, training, evaluation, and deployment-delivering systems that solve real customer pain points. - Productionize solutions: Integrate models into our real-time platform via robust APIs and streaming pipelines, ensuring model performance and guardrails from day one. - Self‑direct & ship: Operate like a founder-set technical roadmap, validate quickly, and iterate based on real-world results.

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