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About This Role
AI / LLM Developer (Vibe Coder) — Build Civra’s Small-Model System
Full-time, in person at 44 Wall Street, New York, NY 10005.
No college degree required.
Build something that works. Own what you ship.
Civra is looking for a hands-on vibe coder who can turn real business data into reliable AI software. Our mission is to adapt and deploy small language models for Civra’s workflows, connect them to databases and APIs, and reduce our dependence on expensive model calls while preserving quality.
You can be an experienced LLM engineer or a strong software developer ready to learn model training. You must already be able to write, understand, debug, test and deploy working code. Show us a project you can explain from end to end.
THE WORK
- Prepare authorized data for training: clean, label, deduplicate and version datasets, with separate training and evaluation sets.
- Evaluate existing small models against Civra’s actual tasks. Fine-tune with approaches such as supervised fine-tuning and LoRA/QLoRA when experiments justify it.
- Build repeatable evaluations for accuracy, grounded answers, structured outputs and tool use. Investigate failures instead of relying on impressive demos.
- Deploy model inference behind reliable APIs. Explore quantization, batching, caching and routing to control latency and total operating cost.
- Connect SQL databases, business APIs and retrieval systems with authentication, permissions, validation and useful logs.
- Use Codex and other AI coding tools to move quickly, while reviewing generated code and owning tests, security and deployment.
WHAT YOU NEED
- Strong practical coding ability in Python or JavaScript/TypeScript, plus SQL. Python will be central to model work; be ready to learn it quickly if your strongest language is TypeScript.
- Evidence of a working application or service with APIs, a database, Git, debugging, testing and deployment.
- The ability to explain your own technical decisions and fix failures without treating generated code as a black box.
- Care with private data, credentials and database access.
- Initiative, clear communication and follow-through. You do not need to know everything. You do need to care about getting it right.
- Availability for full-time, in-person work at our Wall Street office.
STRONG PLUS, NOT A REQUIREMENT
Hands-on PyTorch or Hugging Face work, training-data pipelines, LoRA/QLoRA, model evaluation, local model serving, GPU profiling or measured inference-cost reductions. Strong developers who can learn these skills are welcome. Self-taught builders are welcome.
WHAT SUCCESS LOOKS LIKE
A reproducible data pipeline, an honest baseline, an evaluated small-model prototype, and a working API connected to Civra’s systems. Show the quality, latency and cost tradeoffs with numbers. The goal is lower total cost; training and hosting still have costs.
We supply the tools, resources and business context. You bring the engineering judgment and drive to build.
APPLY WITH
- A GitHub repository, demo or project you built, and exactly what you personally implemented.
- One example of debugging or testing AI-generated code.
- Any model training, evaluation or local-serving experience. If you are new to it, explain how you would learn and test a small-model prototype.
- Your earliest start date and confirmation that you can work full-time in person at 44 Wall Street.
Show us working code. Tell us what broke, how you fixed it, and what you would build next.
Pay: $55,000.00 - $120,000.00 per year
Application Question(s):
- Share one working project or repository involving an API and a database. What did you personally implement, how did you test it, and what difficult bug did you fix? Explain how you used Codex or other AI coding tools, if applicable.
- Have you fine-tuned, evaluated or deployed a language model? If yes, give the model, dataset, your contribution, evaluation method and measured cost/latency. If you are new to model training, describe how you would learn and test a small-model prototype. Prior fine-tuning experience is not required for strong developers.
- Can you work full-time in person at 44 Wall Street, New York, NY 10005? What is your earliest available start date?
Work Location: In person
Salary Context
This $55K-$120K 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 AMRK Enterprises, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $55K to $120K.
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.
AMRK Enterprises AI Hiring
AMRK Enterprises has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $120K - $120K.
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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