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About This Role
Location: Need to be able to work EST timezone.
Remote \| Full\-time
Compensation: $175K \- $250K
We are hiring on behalf of our client who is developing a cutting\-edge autonomous agent runtime focused on high\-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real\-time market outcomes.
The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high\-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth.
Key Responsibilities:
Learning \& Optimization
- Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing.
- Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise.
- Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously.
- Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real\-time.
Autonomous Fleet Intelligence
- Fleet Monitoring: Create higher\-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents.
- Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design.
- Coordination \& Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies.
Model \& Inference
- Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine\-tuned, domain\-specific models.
- Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable.
- Domain\-Specific Training: Determine the efficacy of domain\-specific training over general\-purpose prompting and build the necessary pipelines for implementation.
- Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost\-efficiency at scale.
Requirements
Essential Qualifications
- Production ML Engineering: Proven experience training, deploying, and maintaining models that run in production and directly impact business outcomes.
- Reinforcement/Online Learning: Deep understanding of the practical challenges of learning from real\-world outcomes rather than static datasets.
- Closed\-Loop Systems: A track record of building systems where predictions lead to actions that generate outcomes, which then feed back into improved predictions.
- Software Engineering: Proficiency in Python is required, with additional comfort in Go or TypeScript for production services. Experience building data pipelines and distributed systems is essential.
Preferred Experience
- Financial ML: Background in signal generation, alpha research, portfolio optimization, or execution.
- LLM Specialization: Experience with fine\-tuning and serving (PEFT/LoRA, vLLM, TGI) or custom inference pipelines.
- Multi\-Agent Systems: Experience designing environments where autonomous agents coordinate or learn from one another.
- Domain Knowledge: Background in on\-chain data, DeFi protocols, or sectors where agents make sequential decisions under uncertainty (e.g., robotics, game AI).
Benefits
- Base Salary: $175,000 – $250,000 USD (dependent on location and experience).
- Equity: Approximately 1% initial stock grant, with significant valuation growth potential.
- Performance Incentives: Eligibility for salary increases and bonuses tied directly to revenue and usage.
- Token Participation: Pro\-rata participation in the client’s planned 2026 token launch.
- Ownership: High\-impact role with meaningful upside tied directly to the success of the autonomous fleet.
Interview Process:
- Initial Interview: Discussion with the Founder/CEO.
- Technical Assessment: A take\-home test to evaluate practical application.
- Technical Interview: Deep dive into engineering and ML capabilities.
- Final Interview: Comprehensive final review.
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 $175K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($212K) sits 27% above the category median. Disclosed range: $175K to $250K.
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 New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 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).
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 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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