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About This Role
Voleon Securities, a new business within the Voleon Group, provides liquidity in securities markets. We apply state\-of\-the\-art AI/ML techniques to construct our market making strategies. For nearly two decades, our affiliate Voleon Capital Management has led the hedge fund industry and worked at the frontier of applying AI/ML to investment management, becoming a multibillion\-dollar asset manager. Voleon Securities builds on Voleon’s deep real\-world experience applying ML to financial markets.
Voleon Securities is looking to add an experienced and creative reinforcement learning researcher to our growing ML research group. We welcome researchers with both strong theoretical foundations and experience developing and scaling RL in industrial settings. Financial applications of RL are challenging and demand new methods that go beyond the academic state\-of\-the\-art; it is critical candidates have a deep understanding of the field to draw inspiration for new methodology.
This is a chance to join the initial buildout of a fully modern securities business rooted in the frontier of AI/ML and statistics. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals.
As a Member of Research Staff, you will work at the forefront of modern statistical machine learning. Your research colleagues across Voleon have collectively published hundreds of academic articles in top\-tier venues on machine learning, systems, and theory, and we meet regularly to stay current on the latest academic research and share ideas. Founded in 2007 by two leading scientists, Voleon supports a culture of curiosity, collegiality, and creativity.
Your work will focus on financial market prediction and portfolio optimization. The behavior of financial markets is noisy and violates a number of classical statistical assumptions, and we’ve spent over a decade pioneering scientific advances in the application of machine learning techniques to this domain. You will work with a complex and diverse array of datasets to implement and iterate on predictive models. Predicting financial markets is an enduringly hard problem, but results are immediate and unambiguous.
Years of academic training has prepared you for this moment. You won’t just conduct research, you’ll apply it on a daily basis, working with a team across the entire life cycle of applied research problems. Your work will span from basic research to productizing solutions and validating their efficacy in live trading.
\*\*\* Relocation and work visa eligibility for qualified candidates\*\*\*
Responsibilities
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- Develop a rich understanding of Voleon’s challenges and methodologies and propose RL research innovations and experiments to build, maintain and optimize the models that govern our trading strategy
- Prepare and analyze new datasets to assess their predictive efficacy
- Develop, validate, and implement new models in production
- Design and conduct experiments to improve simulations and evaluate the success of new models in a live environment
- Communicate and collaborate effectively with other Members of Research Staff and Software Engineers at each stage, driving progress towards tangible outcomes
- Keep up to date on the latest RL academic research to identify novel approaches to explore for application to our domain
Requirements
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- Background in reinforcement learning or optimal control with a strong track record of publishing RL papers in top tier conferences
- Evidence of strong mathematical abilities (e.g., publication record, graduate coursework, or competition placement)
- Interest in software development techniques and willingness to write production\-level code (Python)
- Ability to solve large\-scale computing problems with GPUs
- Eagerness to work in a fast paced and growing business
- Interest in financial applications is essential, but prior finance industry experience is not a pre\-requisite
- Ph.D. level coursework is required, and a Ph.D. degree in a relevant field is preferred
“Friends of Voleon” Candidate Referral Program
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If you have a great candidate in mind for this role and would like to have the potential to earn $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program.
Equal Opportunity Employer
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The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.
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Compensation Range: $250K \- $275K
Salary Context
This $250K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At The Voleon Group, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($262K) sits 42% above the category median. Disclosed range: $250K to $275K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
The Voleon Group AI Hiring
The Voleon Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $275K - $275K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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