Senior Software Engineering Educator - AI Enablement
Teaching hospitals operate on a model that's remained largely unchanged for over a century: residents learn medicine not through lectures alone, but through supervised practice alongside attending physicians who've seen thousands of cases. The attending physician doesn't just demonstrate technique—they narrate their clinical reasoning, point out subtle patterns the resident would miss, and know when to let the resident struggle versus when to intervene. This model works because medicine recognized early that expertise requires both knowledge transfer and judgment development, and judgment only develops through guided repetition.
Software engineering is facing a version of this same challenge. AI coding tools can now produce working code faster than most engineers can type it—but a growing body of research suggests that speed comes at a cost. Engineers who lean heavily on AI during skill formation score significantly lower on debugging and conceptual mastery. The gap is especially pronounced in exactly the skills that matter most for oversight: understanding why code works, catching when it doesn't, and reasoning about what should change. As AI-written code becomes the norm, the engineers who can actually verify, debug, and guide that code become more valuable—not less.
Our Engineering Training team is looking for a software engineer and educator whose primary mission is helping Mercury engineers use AI effectively without eroding the fundamentals that make them effective in the first place. You'll be a part of designing the systems, norms, and learning experiences that turn 'AI as accelerant, not dependency' from a slogan into an operational reality—across experience levels, from new hires through senior ICs. You balance pragmatic execution with thoughtful program design, and you're comfortable doing the organizational legwork to drive adoption. This is a unique opportunity to shape what AI-enabled engineering excellence looks like at Mercury.
*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
As part of this role, you will:
Define and operationalize Mercury's AI-usage guidelines for engineering: what engineers should use AI for, what they shouldn't, and how those boundaries shift as skill and context deepen
Design structured checkpoints and assessment frameworks that detect when AI reliance is accelerating growth versus eroding foundational skills like debugging, code reading, and system reasoning
Create clear 'when to unlatch AI' triggers for onboarding and training—criteria that tell engineers and their managers when someone has built enough foundation to lean more heavily on AI tooling
Build and iterate on AI-aware training materials that model the right balance: hand-crafted coding where it builds understanding, AI-assisted workflows where it multiplies leverage
Partner with managers and lead engineers across experience levels to embed AI-usage norms into 1:1s, growth conversations, and performance discussions—not as a separate initiative, but as part of how Mercury engineers develop
Stand up and evolve a mentorship-focused initiative for software engineers, ensuring mentors model thoughtful AI usage alongside strong engineering craft
Do the operational work that drives adoption: scheduling, facilitation, follow-ups, and iteration based on feedback
Collaborate closely with training team members and cross-functional partners to drive broader skill acquisition efforts
The ideal candidate for this role:
Has 5+ years of shipping quality software into production while mentoring peer software engineers in a start-up environment
Has hands-on experience with AI coding tools and a thoughtful, opinionated perspective on where they help and where they hinder
Communicates clearly and gives actionable, direct, kind feedback
Enjoys turning fuzzy goals into simple, repeatable programs
Knows when to lean into 1:1 sessions or organizational legwork to drive adoption or improve learning outcomes
Models a care of craftsmanship and healthy engineering habits—including deliberate, principled AI usage rather than reflexive reliance
Loves turning passive, explanatory content into active, exercise-centric learning resources
Is energized by the tension between productivity and skill development, and sees designing for both as a core challenge rather than a tradeoff to accept
Haskell experience or strong willingness to learn Mercury's primary stack is a plus
If this role interests you, we invite you to explore our public demo at demo.mercury.com and read mercury.com/blog/escalating-esqueleto from our training team. If How AI Assistance Impacts the Formation of Coding Skills resonated with you, we'd especially love to talk.
The total rewards package at Mercury includes base salary, equity (stock options), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate's experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location): $166,600 - $208,300
Canadian employees (any location): CAD 157,400 - 196,800
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.
[Please see the independent bias audit report covering our use of Covey for more information.]
#LI-ME1
About the job
Apply for this position
Senior Software Engineering Educator - AI Enablement
Teaching hospitals operate on a model that's remained largely unchanged for over a century: residents learn medicine not through lectures alone, but through supervised practice alongside attending physicians who've seen thousands of cases. The attending physician doesn't just demonstrate technique—they narrate their clinical reasoning, point out subtle patterns the resident would miss, and know when to let the resident struggle versus when to intervene. This model works because medicine recognized early that expertise requires both knowledge transfer and judgment development, and judgment only develops through guided repetition.
Software engineering is facing a version of this same challenge. AI coding tools can now produce working code faster than most engineers can type it—but a growing body of research suggests that speed comes at a cost. Engineers who lean heavily on AI during skill formation score significantly lower on debugging and conceptual mastery. The gap is especially pronounced in exactly the skills that matter most for oversight: understanding why code works, catching when it doesn't, and reasoning about what should change. As AI-written code becomes the norm, the engineers who can actually verify, debug, and guide that code become more valuable—not less.
Our Engineering Training team is looking for a software engineer and educator whose primary mission is helping Mercury engineers use AI effectively without eroding the fundamentals that make them effective in the first place. You'll be a part of designing the systems, norms, and learning experiences that turn 'AI as accelerant, not dependency' from a slogan into an operational reality—across experience levels, from new hires through senior ICs. You balance pragmatic execution with thoughtful program design, and you're comfortable doing the organizational legwork to drive adoption. This is a unique opportunity to shape what AI-enabled engineering excellence looks like at Mercury.
*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
As part of this role, you will:
Define and operationalize Mercury's AI-usage guidelines for engineering: what engineers should use AI for, what they shouldn't, and how those boundaries shift as skill and context deepen
Design structured checkpoints and assessment frameworks that detect when AI reliance is accelerating growth versus eroding foundational skills like debugging, code reading, and system reasoning
Create clear 'when to unlatch AI' triggers for onboarding and training—criteria that tell engineers and their managers when someone has built enough foundation to lean more heavily on AI tooling
Build and iterate on AI-aware training materials that model the right balance: hand-crafted coding where it builds understanding, AI-assisted workflows where it multiplies leverage
Partner with managers and lead engineers across experience levels to embed AI-usage norms into 1:1s, growth conversations, and performance discussions—not as a separate initiative, but as part of how Mercury engineers develop
Stand up and evolve a mentorship-focused initiative for software engineers, ensuring mentors model thoughtful AI usage alongside strong engineering craft
Do the operational work that drives adoption: scheduling, facilitation, follow-ups, and iteration based on feedback
Collaborate closely with training team members and cross-functional partners to drive broader skill acquisition efforts
The ideal candidate for this role:
Has 5+ years of shipping quality software into production while mentoring peer software engineers in a start-up environment
Has hands-on experience with AI coding tools and a thoughtful, opinionated perspective on where they help and where they hinder
Communicates clearly and gives actionable, direct, kind feedback
Enjoys turning fuzzy goals into simple, repeatable programs
Knows when to lean into 1:1 sessions or organizational legwork to drive adoption or improve learning outcomes
Models a care of craftsmanship and healthy engineering habits—including deliberate, principled AI usage rather than reflexive reliance
Loves turning passive, explanatory content into active, exercise-centric learning resources
Is energized by the tension between productivity and skill development, and sees designing for both as a core challenge rather than a tradeoff to accept
Haskell experience or strong willingness to learn Mercury's primary stack is a plus
If this role interests you, we invite you to explore our public demo at demo.mercury.com and read mercury.com/blog/escalating-esqueleto from our training team. If How AI Assistance Impacts the Formation of Coding Skills resonated with you, we'd especially love to talk.
The total rewards package at Mercury includes base salary, equity (stock options), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate's experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location): $166,600 - $208,300
Canadian employees (any location): CAD 157,400 - 196,800
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.
[Please see the independent bias audit report covering our use of Covey for more information.]
#LI-ME1