Interested in this AI/ML Engineer role at VASERJOB?
Apply Now →Skills & Technologies
About This Role
Applied AI Engineer
New York · Full\-time · All levels, new grad through very senior (we calibrate the role to the person, not the person to a band)
Open to Us Citizen and Permanent Residents
NO visa sponsorship or F1
Who we are
We are a fintech company building AI agent products for institutional investors: hedge funds, asset managers, insurers, brokers, and family offices. Analysts and portfolio managers at institutions in the US, Asia, and Europe use us every day for real research work, including single\-name analysis, earnings and filings interpretation, due diligence, portfolio analytics, and market briefings.
Our core teams are in New York and Seoul, with members in the UK, Singapore, and Hong Kong. We work alongside in\-house finance domain experts, including former buy\-side and sell\-side analysts.
What this team does
The Applied AI Engineering team sits between our customers and our core product.
We work directly with customers to define their problem space, then R\&D and build the solution on top of the core product, extending the core product itself where needed. From there we generalize it into a reusable core capability rather than leaving it as a one\-off.
The scope is wide and deep. Wide across delivery surfaces: the web app UI, the public APIs, and MCP. Deep through every layer beneath them: the AI services, the agent workflows and the search layer they run on, and the data pipeline underneath, from connectors and ingestion through parsing and indexing. An Applied AI Engineer owns that whole stack for the customers they're responsible for, and goes as far down as the problem requires.
Examples of the work
A global broker. Onboarded their full research catalog, building the connector and ingestion pipeline end to end. Developed the search layer over it within our agentic system, covering entity resolution, filtering, and source citation. Expanded the product into an entirely new data domain, built for reuse across other clients.
A European asset manager. Onboarded their internal research data and built a custom connector giving the agent access to their licensed third\-party data. Shipped new product features on request, stood up dedicated regional infrastructure for data residency, and drove reliability and latency work as usage scaled.
A global asset manager. Own the API powering their internal research search. Integrated a new content type into the pipeline, reworking document processing and indexing at scale. Delivered a major search latency reduction, and built the front\-end citation viewer for source visibility.
A multi\-asset family office. Integrated their portfolio systems as data sources for the agent. Developed a portfolio analytics agent over client holdings and built new interactive dashboards on top. Expanded data coverage through new vendor partnerships to close gaps in the existing sources.
Our API platform. Designed and built the public search and analytics APIs, along with the data pipeline underneath. Continue to expand what the core product exposes to customers building on top of it.
What you'll do
- Own a customer problem from definition through production. Work directly with the customer to define the problem space, R\&D the solution, build it, ship it, and keep it running.
- Build on the core product, and extend the core product itself. Solving a customer problem often requires a capability the product doesn't have yet, and building that is part of the work.
- Generalize what you build. Take what starts as one client's solution and turn it into a core capability other clients can use, rather than accumulating one\-offs.
- Design and build agentic systems. Prompts, tools, and multi\-step agent workflows, along with the search and retrieval layer feeding them. Agents are the primary consumer of what you build, so search gets designed around how they actually plan and read.
- Own the data layer underneath. Connectors and ingestion, parsing, chunking, metadata extraction, entity resolution, and indexing across OpenSearch and PostgreSQL. How documents become retrievable determines the ceiling on answer quality.
- Own it after it ships. Debug production issues, trace real usage to find where quality is breaking, and fix root causes rather than patching. That includes existing systems, not just what you build.
- Turn the core product into APIs. Harden the internal skill and tool systems and expose them in forms external systems can build on.
- Make improvements measurable. Search precision and recall against labeled query sets, extraction quality evals, LLM\-as\-judge, and citation grounding checks. For financial answers, source attribution is a hard requirement.
- Go down the stack when the problem is there. External integrations, backend services, data pipelines, and the runtime they execute on. Everything is infrastructure as code, so you make those changes yourself.
- Develop with coding agents as a core practice. Claude Code, Cursor, Codex, used to multiply your own throughput.
Who we're looking for
- You've owned a system from the data layer through search to the surface it's served on. Ingestion, pipeline, retrieval, and the API or UI on top, rather than a single layer of it.
- You've built agentic or LLM systems in production, at scale. Not a prototype or an internal MVP, but systems running against real data volume and real users, where you handled the failure modes that come with it.
- You think in agentic retrieval, not just traditional RAG. You understand why an agent scoping and iterating its own search behaves differently from a fixed retrieval pipeline, and you design for that consumer.
- You're comfortable with document processing at scale. Ingestion, parsing, chunking, metadata extraction, entity resolution, and indexing that feeds a production search product.
- You adapt quickly when the architecture stops fitting. This space changes every few months, and requirements shift with it. We look for people who will redesign a system when that happens rather than defend the existing one.
- You're hands\-on and applied rather than research\-oriented. Backend engineering in Python or TypeScript, with a track record of delivered systems.
- You structure ambiguous problems yourself and move without waiting for a spec.
- You've used coding agents deeply (Claude Code, Cursor, or equivalent) as a core part of how you work.
- You can communicate with teams globally, and work directly with customers, domain experts, and our Seoul team.
Nice to have
- Interest in finance and markets, and genuine enjoyment of digging into data.
- Experience with search and retrieval systems: ranking, hybrid retrieval, re\-ranking, relevance evaluation.
- Experience building evaluation and monitoring systems, especially citation and source grounding verification.
- Experience with external system integration, connectors, and data pipelines.
- Experience designing and serving B2B products directly to financial institution customers.
- Hands\-on cloud experience (AWS, Azure, or GCP), Kubernetes, GitOps, and IaC, plus a view on how to combine them with agents.
- NLP, ML, or statistics background.
Tech stack
You don't need all of this on day one. The essentials are Python, one search or data store, and a willingness to go down the stack.
- Python: FastAPI, Pydantic, SQLAlchemy/SQLModel, asyncio, Celery for orchestration
- TypeScript: the MCP tool server exposing search and data tools to agents
- AI layer: multi\-model chains (OpenAI, Anthropic, Gemini) with structured outputs, fallback policies, and cost accounting; embeddings for vector search; LLM\-as\-judge and scoring harnesses
- Data and search: OpenSearch (BM25 and vectors), PostgreSQL, S3
- Infra: Kubernetes, Terraform, Helm, ArgoCD on AWS
Culture \& benefits
- A flat culture where we address one another by name, without job titles
- Flexible start times (8 to 10 am)
- Self\-development support (books, courses, seminars)
- Lunch and dinner provided
- Free snack bar and beverages
- Latest MacBook and monitor
- Outstanding colleagues who are top experts in their fields.
Because our teams span New York and Seoul, the New York team syncs with Seoul in the evening ET.
Hiring process
Application → Introductory Interview → Technical Round Intro → Technical Assessment → Follow\-up Technical Interview → Culture\-Fit Interview → Offer
Pay: $90,000\.00 \- $260,000\.00 per year
Application Question(s):
- Are you U.S. Citizen or Permanent Resident?
Work Location: In person
Salary Context
This $90K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At VASERJOB, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $90K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
VASERJOB AI Hiring
VASERJOB has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $260K - $260K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.