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About This Role
Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.
The AI Studio is the team responsible for designing, building, and operationalizing internal software platforms across every functional department of the company, including People Operations, Customer Support, Sales, Marketing, Finance, Recruiting, and Executive Operations. We treat each internal department as its own product surface, and in truth as its own startup, with its own customers, its own metrics, and its own reasons for existing. We ship custom software, built on Replit’s own platform, to solve operational problems that off\-the\-shelf SaaS tools cannot serve well at our scale and pace of growth.
Position Summary
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The Member of Technical Staff (AI Builder) is a full\-time builder role with substantial autonomy. You will design, architect, and ship AI\-powered internal platforms that run the operations of the company across many departments at once. This is not a role for someone who wants a narrow, well\-defined lane. It is a role for someone who wants to walk into an ambiguous business problem, understand it well enough to argue about it with the department lead, and then ship the software that fixes it.
We are looking for people who genuinely understand the mechanics of a business: how revenue is actually made, why recruiting velocity matters, what makes support scale or break, how finance closes a month, and why each department is critical to whether the company wins or loses. You do not need to have run every function, but you need to respect why each one exists and be able to reason about it like an operator, not just an engineer.
Because we treat each department as its own startup, we strongly favor people who have either run their own business or worked on a fast\-scaling startup. You know what it feels like to build with incomplete information, to ship before it is comfortable, and to own an outcome rather than a ticket. The work is genuinely a ton of fun, and it is also demanding: it rewards diplomacy, creativity, technical depth, and business acumen in roughly equal measure. You will move fast, work long hours when it matters, and switch fluidly between a deep technical conversation and a conversation with a non\-technical stakeholder who just needs their problem solved.
Key Responsibilities
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Internal Platform Design and Engineering
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- Design, architect, and ship custom AI\-powered internal platforms that serve specific operational needs across Replit’s functional departments, including but not limited to People Operations, Customer Support, Sales, Recruiting, Marketing, Finance, and Executive Operations.
- Translate ambiguous, often non\-technical business requirements into technical specifications, system architectures, and shipped software products.
- Own platform development end\-to\-end, including front\-end interfaces, back\-end services, data models, third\-party integrations, and AI model orchestration.
Applied AI and Large Language Model Engineering
- Architect multi\-stage AI pipelines incorporating large language models, including prompt engineering, tool\-call architecture, structured output generation, retrieval\-augmented generation, and agentic workflows.
- Conduct model selection across multiple frontier and open\-source providers, evaluating models against cost per query, latency, output quality, reliability, and task\-specific performance.
- Design and implement evaluation frameworks for AI features, including test datasets, scoring rubrics, and regression tests, so that AI\-driven workflows stay reliable in production.
- Optimize AI systems for cost and performance, including the appropriate use of lighter\-weight models for qualification passes, batching strategies for high\-throughput workloads, and caching architectures.
Business Understanding and Stakeholder Partnership
- Treat each internal department as its own startup: understand its goals, its constraints, and the metrics that define whether it is succeeding.
- Partner directly with department leads to identify operational inefficiencies, scope solutions, and prioritize the work that creates the most leverage.
- Conduct stakeholder interviews, lead requirements\-gathering sessions, and translate qualitative feedback into iterative product improvements.
- Present technical work and operational outcomes to executive leadership, including the President and senior staff, and do it in language each audience can act on.
Third\-Party System Integration
- Design and implement integrations with the third\-party platforms used at Replit, including enterprise HR platforms, support ticketing systems, CRM platforms, applicant tracking systems, project management tools, and identity providers.
- Evaluate third\-party APIs, SDKs, and data schemas, and design integration architectures appropriate to scale, security, and maintainability requirements.
Production Operations and Reliability
- Maintain and operate shipped platforms in production, including dependency management, security updates, observability, error handling, and performance monitoring.
- Respond to production incidents and continuously iterate on shipped platforms based on user feedback and operational telemetry.
Documentation and Knowledge Transfer
- Document architectural decisions, system designs, and operational procedures for shipped platforms.
- Onboard future team members and contribute to the evolution of the AI Studio as the function scales.
Who You Are
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- You understand how a business actually works and can explain why each department matters to the company winning.
- You have either run your own business or worked on a fast\-scaling startup, and you carry the ownership instinct that comes with it.
- You are comfortable with long hours and a very high pace, and you treat that as part of the fun rather than a cost.
- You can engage credibly with both deeply technical people and non\-technical stakeholders, often in the same afternoon.
- You bring diplomacy, creativity, technical knowledge, and business acumen, and you know when each one is the tool the moment calls for.
- You would rather ship something real and iterate than wait for perfect information.
Experience and Qualifications
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- Bachelor’s or master’s degree in Technology Management, Computer Science, Software Engineering, Information Systems, or a closely related technical or technology\-business hybrid field. Equivalent foreign degrees acceptable subject to credential evaluation. Exceptional non\-traditional backgrounds with a strong shipped portfolio will be considered.
- A demonstrated portfolio of shipped software products, including AI\-powered applications, and the ability to discuss technical architecture, design trade\-offs, and engineering decisions in detail.
- Experience either running your own business or working at a fast\-scaling startup, with real exposure to how the business operated end\-to\-end.
- Working proficiency in modern software engineering practices, including version control, code review, deployment pipelines, and production debugging.
- Hands\-on experience with one or more large language model providers and their APIs, tool\-calling interfaces, and structured output mechanisms.
- Demonstrated experience designing prompt and tool\-call architectures, conducting model selection across providers, and optimizing AI systems for cost and latency.
- Programming proficiency in Python and TypeScript or JavaScript, including familiarity with relevant frameworks for AI application development.
- Experience with relational databases, SQL, and modern data tooling.
- Strong written and verbal communication skills, including the ability to present technical work to non\-technical stakeholders.
- Demonstrated ability to operate with autonomy in ambiguous, fast\-moving environments.
Preferred Qualifications
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- Prior experience as a founder, co\-founder, or early team member at a venture\-backed startup, particularly one with an AI\-native product.
- Prior experience in technology investment analysis or operational consulting, with a demonstrated ability to evaluate business operations and identify high\-leverage interventions.
- Hands\-on experience building on the Replit platform, including familiarity with Replit Agent and Replit Deployments.
- Experience designing and shipping internal tools at a high\-growth technology company.
- Multilingual proficiency, particularly in languages relevant to Replit’s international customer base, including Italian, Portuguese, Spanish, or other major commercial languages.
- Experience with mobile application development frameworks, including React Native and Expo.
Travel and Location Requirements
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This position is based in Foster City, California, with secondary work performed at the company’s San Francisco, California office. The company operates on a hybrid in\-person schedule, with employees expected to be in\-office Monday, Wednesday, and Friday in Foster City, and Tuesday and Thursday in San Francisco. Occasional domestic travel may be required for company offsites and customer engagements.
Full\-Time Employee Benefits Include:
Competitive Salary \& Equity
401(k) Program with a 4% match (*US Only*)
Health, Dental, Vision and Life Insurance
Short Term and Long Term Disability
Paid Parental, Medical, Caregiver Leave
Flexible Time Off (FTO) \+ Holidays
Commuter Benefits (*In\-Office Only*)
Monthly Wellness Stipend
Autonomous Work Environment
In Office Set\-Up Reimbursement (*In\-Office Only*)
Quarterly Team Gatherings
In Office Amenities (*In\-Office Only*)
Want to learn more about what we are up to?
- Meet the Replit Agent
- Replit: Make an app for that
- Replit Blog
- Amjad TED Talk
Interviewing \+ Culture at Replit
- Operating Principles
- Reasons not to work at Replit
To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non\-traditional backgrounds.
Compensation Range: $200K \- $275K
Salary Context
This $200K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Replit, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($237K) sits 9% above the category median. Disclosed range: $200K to $275K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Replit AI Hiring
Replit has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Foster City, CA, US. Compensation range: $275K - $310K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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