Member of Technical Staff - Applied AI

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• 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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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

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

Schedule

Shift type

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Weekend work

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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

Member of Technical Staff - Applied AI Toronto About Steel Steel is building open-source browser infrastructure for AI agents and apps . We make it easy for developers to ship AI products that interact with the web using our Sessions API . With over 6,000 GitHub stars, dozens of paying customers, and millions of sessions served monthly, we grew our platform 50x in 2025 purely through word-of-mouth and our open-source community. Backed by world-class investors, we're building a small, highly motivated, talent-dense team that's shaping the future of how humans interact with the internet. More on the problems we're solving: https://docs.steel.dev/overview/intro-to-steel Public referral bonus: Introduce us to a candidate who joins Steel and we'll send you $10,000 The Role We're looking for an exceptional engineer to shape how AI agents interact with the web by building the open-source frameworks/demos and shaping the developer experience that makes Steel indispensable for AI developers. Sample projects include... Building developer tools to run browser agents on top of Steel's Sessions API Running experiments to learn which models/prompts/harnesses actually work for browser automation Building eval sets to rigorously benchmark agents against real-world scenarios Creating open-source demos that show what's possible with AI agents and browsers Working with customers to help them deploy custom agents into their workflows Writing a technical post unpacking learnings from a 2-day debugging session Requirements: 4+ years writing high-quality, production code AI-native - a heavy user of AI agents in day-to-day coding workflows Experience shipping full-stack product features end-to-end and enjoy

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