Join Cognition as an Applied AI Engineer to drive the adoption of AI solutions in enterprise engineering teams.
Posted by employer 11 months ago
First seen on Joblaze 2 days ago
Last verified on the company career page 8 hours ago
Skills & Technologies
What you'll build
Must have
Nice to have
AI in the day-to-day
You embed with engineering teams, integrate agentic workflows into how they actually build and ship.
Requirements
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
The Applied AI Engineer at Cognition focuses on embedding AI solutions within enterprise engineering teams to enhance productivity and drive adoption of their AI software, Devin. This role requires strong coding skills in languages like Python or JavaScript, along with a background in software engineering or technical consulting. Ideal candidates are those with experience in driving technical adoption and a knack for communicating complex concepts effectively. Cognition's team is composed of highly skilled professionals from leading AI companies, fostering a collaborative environment aimed at solving significant challenges.
Joblaze insights
Quick facts
From the original posting
Applied AI Engineers are how Cognition operationalizes AI modernization at the enterprise level. You don't demo Devin - you deploy it. You embed with engineering teams, integrate agentic workflows into how they actually build and ship, and drive the kind of measurable productivity gains that make adoption irreversible.
As one of our first Applied AI Engineers, you'll also shape the function itself - turning individual customer engagements into repeatable playbooks, digital learning content, and partner-driven enablement models that allow Cognition to reach hundreds of thousands of engineers worldwide.
This role is perfect for someone who has strong engineering fundamentals, enjoys working directly with customers, and finds deep satisfaction in driving product adoption and having users unlock their potential with Devin - while also thinking systematically about how to scale those learnings to the next 100 teams.
Embed with enterprise engineering teams to drive deep, lasting adoption of Devin — owning outcomes, not just onboarding
Architect and implement agentic workflows across engineering, QA, support, data, and product — identifying where AI creates the highest leverage and building toward it
Lead interactive programs for enterprise engineering teams (live workshops, pair programming sessions)
Guide customers through installing, configuring, and optimizing Devin and its associated tools (DeepWiki, MCP integrations, etc.)
Pair-program on live production problems to demonstrate high-value usage patterns and accelerate the team's applied AI fluency
Quantify impact — tracking productivity metrics, surfacing ROI stories, and making the business case for expanding Devin's footprint within accounts
Work with leadership to scale field learnings into structured playbooks and best practices that scale beyond individual engagements
Create enablement materials, best practices, and shared playbooks based on customer learnings
Degree in a STEM field or equivalent hands-on experience
3+ years as a software engineer, technical consultant, deployment strategist, forward deployed engineer, solutions engineer or similar roles with strong coding proficiency (Python, JavaScript/TypeScript, or similar)
Proven ability to communicate complex technical topics to diverse audiences
Proven track record of driving technical adoption and measurable impact inside engineering organizations
Strong commercial instincts; you understand that successful engagements grow accounts, and you operate accordingly
Excellent verbal and written communication skills
Demonstrated ability to learn and adapt exceptionally fast
You've led developer enablement, platform adoption, or internal AI modernization initiatives — and you can point to the before/after metrics
Have deployed or integrated LLM or agent-based systems in production settings
Have previously founded or joined early-stage startups where autonomy and execution speed were critical
You're energized by seeing a team's velocity compound after working with them — and you engineer those outcomes deliberately
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