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About This Role
### About Veho
Veho’s mission is to power the future of commerce by making shopping, shipping and returns seamless for everyone.
We are building a modern, end\-to\-end logistics infrastructure designed entirely for the ever\-evolving needs of ecommerce brands and everyday consumers.
Powered by next\-generation technology and a vertically integrated supply chain, Veho gives brands and their customers unprecedented control over their deliveries and removes the pain from the ecommerce post\-purchase experience. We make delivery the ‘extension of the brand’ and leverage it to create deeper loyalty and trust between brands and their customers, driving customer retention and lifetime value. Our rapidly growing client list includes leading consumer brands like Hello Fresh, Zara, Macy’s, Sephora, and more.
To truly build an iconic company, we strongly believe that our people and values must be aligned with our mission. As such, we take pride in our championship team, merit\-based culture. We seek team players who want to compete, win, make an impact and build a legacy, and we reward performance and impact players with generous equity and incredible career growth opportunities.
About The Role:
As a Senior Machine Learning Operations Engineer you’ll be embedded in a team of talented data scientists and software engineers to create sophisticated models that answer hard questions centered around improving our logistics network and user experiences. This role bridges between ML platform work and building on top of our platforms to create new models. You’ll create the infrastructure and tooling necessary to deploy, monitor, and scale our machine learning models in production. In close collaboration with data scientists you’ll own our production models, ensuring optimal performance and responding to production incidents.
A great candidate:
Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders.
Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short\-term while building out the long term vision.
Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team’s models
What you’ll do:
- Build reliable, efficient, and scalable infrastructure for our AI/ML capabilities
- Create robust data pipelines to feed analyses and models
- Enable forecasting, network orchestration, and live pricing systems
- Ensure data quality and data integrity through best practices in data integration
- Build out robust feature stores, model orchestration tooling, experimentation tooling, model performance monitoring.
- Create standards and templates for model development and deployment across all Data Science teams.
- What You Bring:
- Bachelor’s Degree plus at least 3 years of experience in machine learning engineering, or Master’s Degree plus at least 2 years in machine learning engineering
- This experience should include:
- Developing and optimizing MLOps pipelines for speed, reliability, and observability.
- Utilizing statistical modeling or machine learning techniques to solve business problems.
- Strong proficiency in Python and SQL.
- Hands\-on experience with open\-source languages and tooling for large\-scale ML (e.g., Ray, Flink, Feast).
- Working with Data Warehouses (e.g., Redshift, Databricks, Snowflake).
- Utilizing cloud\-based (AWS Preferred) data engineering and data science tools.
- Experience building ML systems in Startups is a plus
- Experience with DS/ML in Logistics/Supply Chain is a plus.
Veho is a growth company that looks for team members to grow with it. No matter the location, or the role, every Veho teammate shares one galvanizing mission: driving commerce forward with a customer\-centric delivery and returns experience that’s built for the modern era. We are deeply value\-driven (Team Up, Drive Impact, Take Ownership, Solve Bigger, Obsess Over Experience, Make Today Count) and care tremendously about investing in our high\-performers.
Join us in building the future of ecommerce logistics and in doing the work of our lifetime!
All California applicants please reference our California Applicant Privacy Notice located here.
Compensation Range: $175K \- $200K
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Veho, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($187K) sits 14% below the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Veho AI Hiring
Veho has 7 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer. Positions span New York, NY, US, US. Compensation range: $200K - $325K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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