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About This Role
We are looking for a Technical Product Manager to join our AI and Machine Learning Team, focused on delivering innovative, data\-driven solutions across trading, analytics, and enterprise technology platforms.
This role is highly cross\-functional and combines product strategy, project management, stakeholder coordination, and technology delivery within a fast\-paced environment. The successful candidate will work closely with engineering, quantitative research, data science, and business teams to drive the execution of AI and machine learning initiatives from concept through deployment.
Job Responsibilities
- Partner with senior management, engineering, and quantitative teams to define product strategy, prioritize initiatives, and manage the delivery roadmap for AI and machine learning solutions
- Lead cross\-functional projects focused on developing and implementing AI\-driven pricing, analytics, and workflow optimization capabilities across multiple business areas
- Coordinate the end\-to\-end lifecycle of machine learning initiatives, including requirements gathering, project planning, execution tracking, testing, deployment, and post\-release support
- Translate complex business requirements into detailed functional specifications and actionable development plans for technical teams
- Facilitate collaboration between product, engineering, data science, and business stakeholders to ensure timely and successful project delivery
- Monitor project timelines, dependencies, risks, and deliverables while driving issue resolution and maintaining clear communication across teams
- Oversee testing, validation, stress testing, and performance monitoring of AI and machine learning models to ensure scalability, reliability, and business effectiveness
- Support continuous improvement initiatives by identifying opportunities for automation, enhanced analytics, and operational efficiency improvements
Qualifications
- BS, MS, or PhD in Computer Science, Engineering, Mathematics, Data Science, Finance, or a related quantitative discipline
- 2\+ years of experience in technical product management, project management, AI/ML initiatives, or related technology\-focused roles
- Strong understanding of software development lifecycles, agile project management methodologies, and cross\-functional technology delivery
- Familiarity with machine learning concepts, multivariate regression techniques, optimization methods, and data\-driven product development
- Strong analytical and organizational skills with the ability to manage multiple priorities and complex projects simultaneously
- Experience working with Python, SQL, Pandas, or related data analysis and reporting tools is preferred
- Excellent communication and stakeholder management skills, with the ability to bridge technical and business teams effectively
- Experience within financial services, trading platforms, or capital markets environments is beneficial but not required
Additional Information
Tradeweb is committed to providing valuable and competitive benefits. In addition to working in our culture of innovation and collaboration, we offer:
- Health Insurance : Highly competitive medical, dental, and vision programs
- Hybrid Environment : Our employees have the flexibility of working in the office and from home.
- Health Care and Dependent Care Flexible Spending Accounts : You may elect to set aside pre\-tax earnings to pay for eligible health care and dependent day care expenses for you and your eligible family members.
- Maven Family Building Benefit : Maven offers support for fertility and preconception; pregnancy and post\-partum; adoption; surrogacy and pediatrics for children up to age 10\. Tradeweb provide a $10,000 lifetime reimbursement towards fertility, egg freezing, adoption and surrogacy expenses.
- Building Wealth \- 401(k) Savings Plan : Employees are immediately eligible for the 401(k) plan. Participants may contribute up to 75% of eligible compensation into a traditional 401(k) and/or Roth 401(k). Tradeweb will match 100% of the first 4% of compensation that you contribute.
- The current pay range for this role if performed in the city of New York is currently $130,000 to $140,000 per year, based on a regular, full\-time schedule. The amount of pay offered will be determined by a number of factors, including but not limited to qualifications, market data, geographic location, and internal guidelines.
Other Benefit Programs
- Pre\-Tax Commuter Benefits Program
- ARAG Legal Services
- Employee Assistance Program
- Tuition Reimbursement
- Financial Wellness Tools
- Travel Assistance Benefits
- Pet Insurance
- Corporate Gym Subsidies
- Wellness Perks
- Paid Time Off and Parental Leave
Salary Context
This $130K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Tradeweb, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $130K to $140K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tradeweb AI Hiring
Tradeweb has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $140K - $140K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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