Interested in this AI/ML Engineer role at Lime Fintech?
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About This Role
Lime is a financial technology group serving active and institutional traders, with an established product suite spanning execution, market data, and quantitative research infrastructure.
As the firm expands its institutional business, it is investing in the internal systems behind it: onboarding, client-facing portals, data pipelines, and the integrations that connect them. This search supports that build-out.
We are looking for curiosity and tenacity above credentials. Students who perform well will be given increasing ownership over individual modules and a clear path toward a longer-term engagement with the firm.
Location & Format
Location: New York, hybrid (3 days in office, Midtown, next building from Penn Station)
Format: Part-time or full-time options available
Reports to: Department leads
Key Responsibilities
· AI-assisted development – Use AI coding tools (e.g., Claude Code, CodeX, Cursor, GitHub Copilot) as a core part of daily work to design, write, test, and document code, and build LLM-powered steps into internal workflows where they add value
· System integration – Design, build, and maintain API connectors between internal and external databases and systems
· Module development & automation – Support the development and automation of core internal modules, including client onboarding and the client portal
· Data processing & management – Structure, clean, and analyze diverse datasets; set up reliable database connections and keep data logic consistent across systems
· Technical collaboration – Work directly with lead engineers and department heads to translate business logic into working code and automated scripts
· Independent problem-solving – Read documentation, investigate unfamiliar systems, and resolve technical problems independently before escalating
Target Candidate Profile
Fields of Study
· Master’s or upper-level Bachelor’s students in Quantitative Finance, Financial Engineering, Computer Science, Data Science, Physics, Computational Finance, Financial Mathematics,
· Ideally with a minor or concentration in Data Science or Finance
Track Record Beyond Coursework
Preference will be given to students who have shown initiative outside the standard curriculum, for example:
· Math, physics, or programming olympiads
· Coding competitions and hackathons
· Business case challenges
· Active roles in technical, quantitative, or finance student clubs
Requirements
· Analytical rigor – Strong mathematical foundation, logical reasoning, and a structured approach to problem-solving
· Software & data fundamentals – Practical programming experience with a solid grasp of software development principles, APIs, database architecture, and data structures
· Hands-on AI programming experience (required) – Demonstrated experience building software with AI coding assistants and LLM tools; be ready to walk through a concrete project and explain how you used AI to build it and how you verified its output
· Curiosity – Real curiosity about how systems and financial businesses work, evident in projects you started on your own
· Tenacity – The persistence to work through documentation and unfamiliar codebases and to finish difficult tasks without step-by-step guidance
· Coachability – Openness to direct code review and the ability to apply feedback quickly
· Clear written and spoken English
Strong Plus Qualifications
· Experience building with LLM APIs (OpenAI, Anthropic, or similar), prompt engineering, RAG, or agent frameworks
· Experience with REST APIs, webhooks, or integration and automation platforms
· Familiarity with relational databases and basic data modeling
· Personal or academic projects available on GitHub or a portfolio
· Interest in markets, trading, or financial technology
What This Role Is Not
To save everyone time: this is not a quantitative research or trading position. We are looking for strong data skills, sound logic, and solid software engineering fundamentals. Nor is it a role for someone who treats AI tools as optional: they are central to the day-to-day work. Equally, this is not a passive observation role. Students who prefer well-defined tasks with close supervision at every step are unlikely to enjoy it; those who like to take a vague problem, read up, and come back with working code will.
Pay: $25.00 - $35.00 per hour
Benefits:
- Flexible schedule
Work Location: Hybrid remote in New York, NY 10040
Salary Context
This $52K-$72K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Lime Fintech, 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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($62K) sits 71% below the category median. Disclosed range: $52K to $72K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Lime Fintech AI Hiring
Lime Fintech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $72K - $72K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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