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About This Role
Location: Remote \- If based in NYC, will require office presence 2/3 days a week, for LATAM and European regions will be expected to work EST hours with a 5 hour overlap.
Remote/Hybrid \| Full\-time
Compensation: $140K \- $200K
We are hiring on behalf of our client, an innovative technology firm who is seeking an experienced Full\-Stack AI Engineer to join a growing, AI\-focused engineering team. The client develops advanced intelligence and analytics solutions designed to help participants in digital asset markets make more informed decisions by combining financial data, automation, and modern AI technologies to transform complex information into actionable insights.
In this role, the successful candidate will work closely with product, research, and engineering leadership to develop next\-generation, AI\-powered financial applications. This is a highly collaborative position that requires strong ownership, technical depth, and the ability to move quickly from concept to production.
Key Responsibilities
Full\-Stack Product Development
- Architecture \& Maintenance: Architect, develop, and maintain end\-to\-end applications powering AI\-driven financial products.
- Backend \& APIs: Build scalable backend services and APIs that support intelligent workflows and automated decision\-making.
- UI Development: Create intuitive, high\-performance user interfaces that surface complex insights and enable interactive experiences.
- Real\-Time Systems: Design systems that support real\-time communication between users, data sources, and AI components.
AI \& Data Integration
- Cross\-Functional Collaboration: Partner with research and machine learning teams to integrate AI capabilities into production environments.
- Data Pipelines: Implement and maintain pipelines that ingest, process, and manage structured and unstructured financial data.
- Operationalization: Support the deployment and operationalization of AI\-powered features and workflows.
Engineering Excellence
- Standards \& Reliability: Establish testing, observability, monitoring, and reliability standards across applications and services.
- Optimization: Optimize system performance, scalability, and maintainability.
- Technology Evaluation: Evaluate and adopt emerging technologies across AI, software engineering, and financial infrastructure.
Continuous Learning
- Industry Trends: Stay informed on developments in large language models, agent frameworks, financial technologies, and modern web architectures.
- Best Practices: Contribute to technical discussions and help shape engineering best practices across the organization.
Requirements Required Qualifications
- Experience: 5\+ years of professional experience building full\-stack applications.
- Backend Expertise: Strong programming experience with Python, including modern API frameworks such as FastAPI. Advanced proficiency in JavaScript and TypeScript, with extensive experience developing applications using Node.js.
- Frontend Expertise: Advanced proficiency in React for building user interfaces.
- System Design: Demonstrated success building scalable web platforms, APIs, and backend services, alongside a strong understanding of both relational and non\-relational database technologies.
- AI \& Agent Architecture: Deep knowledge of prompt design, tool\-calling architectures, Model Context Protocol (MCP), and agent orchestration patterns. Experience deploying and operating AI agents or autonomous workflow systems in production environments.
- Information Retrieval: Experience building embedding pipelines, semantic retrieval systems, and advanced search capabilities. Familiarity with vector search technologies, retrieval\-augmented generation (RAG), and asynchronous application patterns.
- AI Frameworks: Hands\-on experience developing solutions using large language models and AI orchestration frameworks (e.g., LangChain or comparable technologies).
- Domain Knowledge: Experience working with financial datasets, market data, or financial service APIs.
- Professional Attributes: Ability to operate comfortably in fast\-moving environments with significant autonomy and ownership.
Preferred Qualifications
- Location: Located in the United States, Latin America, or Europe, with flexibility to collaborate across time zones.
- Performance Tuning: Strong understanding of application profiling, scalability optimization, and performance tuning.
- Track Record: A history of success within high\-growth, collaborative engineering organizations.
- Digital Assets: Exposure to blockchain infrastructure, smart contracts, decentralized finance (DeFi), or digital asset ecosystems.
Benefits
- Competitive compensation package.
- Opportunity to work at the intersection of cutting\-edge AI and digital asset markets.
- Highly autonomous and collaborative work environment.
- Remote\-friendly flexibility across eligible regions.
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\[email protected].
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 [email protected].
Salary Context
This $140K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At mLabs, 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($170K) sits 6% below the category median. Disclosed range: $140K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
mLabs AI Hiring
mLabs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $210,300 across 2,585 tracked positions. That's 5% above the national 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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