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About This Role
Full\-Stack Engineer, (AI)Location: NYC preference \- will accept Remote USA to work EST hours \- Will accept LATAM region to work EST hours, and also Tel Aviv
Remote \| Full\-time
Compensation: $160K \- $200K
Our client is a pioneer in automated, on\-chain economic security systems, providing high\-fidelity custom EVM simulations to protect and optimize decentralized finance (DeFi) protocols. Trusted by the industry’s leading platforms to secure and manage billions of dollars in assets, our client enables crypto protocols to refine risk management and capital efficiency through actionable, data\-driven insights.
The organization is seeking a proactive and results\-driven Full\-Stack Engineer to join their AI division. Reporting directly to the Head of Product \& AI, this individual will play a critical role in collaborating with the CEO and the AI team to develop a sophisticated financial AI agent. The ideal candidate thrives in high\-velocity, self\-directed environments and possesses the technical breadth to manage everything from scalable backend services to high\-performance, user\-facing interfaces.
Key Responsibilities
- System Design: Architect and build robust full\-stack systems to power a proprietary AI financial agent.
- API \& Backend Development: Develop scalable APIs and backend services designed to support complex agent workflows.
- Interface Engineering: Build high\-performance user interfaces that visualize agent reasoning, surface deep financial insights, and enable real\-time interaction.
- AI Integration: Collaborate closely with AI researchers to productionize agent logic and integrate Large Language Model (LLM) frameworks.
- Data Management: Connect and manage complex data pipelines sourced from various financial providers.
- Operational Excellence: Implement rigorous testing, monitoring, and performance best practices to ensure system reliability.
- Innovation: Stay at the forefront of LLM frameworks, full\-stack development tools, and evolving financial trends to maintain a competitive edge.
Interview Process
- Initial Screening: 30\-minute call with Recruiter / HR.
- Hiring Manager Interview: 30\-minute discussion with the Head of Product \& AI.
- Technical Evaluation I: 1\-hour session focused on LeetCode\-style challenges (AI tool usage permitted).
- Technical Evaluation II: 1\-hour session focused on syntax and programming fundamentals (AI tool usage permitted).
- Executive Interview: 30\-minute final discussion with the Founder / CEO.
Requirements
- Experience: 5\+ years of professional full\-stack development experience, preferably in fast\-paced or emerging technology sectors.
- Technical Stack: Strong proficiency in Python (FastAPI), JavaScript/TypeScript, Node.js, and React.
- Backend Expertise: Proven experience building scalable APIs, web applications, and a solid knowledge of both relational and non\-relational databases.
- AI \& LLM Proficiency: Hands\-on experience with LLMs and frameworks such as LangChain or similar; familiarity with vector databases, RAG pipelines, and asynchronous programming.
- Domain Knowledge: Professional experience working with financial data and APIs.
- Location: Ability to work from the Brooklyn, NY office five days a week.
Preferred Qualifications:
- Previous experience building and deploying LLM\-based agents in production environments.
- Familiarity with prompt engineering, tool use orchestration (MCP), and semantic search.
- Experience with embedding pipelines and RAG architectures.
- Exposure to smart contracts, blockchain protocols, and DeFi systems.
Benefits
Our client provides a comprehensive benefits package designed to support professional growth and personal well\-being:
- Time Off: Competitive PTO (21 days), sick leave (7 days), and 8 observed US company holidays.
- Health \& Wellness: 100% employer\-paid medical, dental, and vision insurance for employees and their dependents; access to FSA or HSA options.
- Family Support: A thoughtful and inclusive Parental Leave Policy.
- Corporate Wellness: Access to premium programs including OneMedical, Teladoc, Talkspace, and Employee Assistance Programs (EAP).
- Professional Growth: Career advancement opportunities within a rapidly expanding global technology firm, including personalized professional development.
- Commuter Benefits: Pre\-tax commuter benefits to support office\-based work.
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job\-advert in an accessible format please let us know at the earliest opportunity by emailing human\-resources@mlabs.city.
MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.
Salary Context
This $160K-$200K range is below the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At mLabs, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($180K) sits 23% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
mLabs AI Hiring
mLabs has 5 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span US, MA, US, FL, US. Compensation range: $200K - $250K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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