Interested in this MLOps Engineer role at Veho?
Apply Now →Skills & Technologies
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:
Veho’s Data Science team is core to Veho’s ability to deliver millions of packages by creating the systems that drive forecasting, network orchestration, pricing, and routing decisions. Being part of this team, the Machine Learning Operations team drives the foundation of these systems by being a partner to the data scientists to create well\-designed, stable, and performant systems.
As the Technical Lead Manager, you’ll own our Data Science platform and our 1\-2 year roadmap for creating a sophisticated and stable platform that keeps up with Veho’s rapid growth. You and your team embed into science projects so engineering quality is built in from day one, and create the templates to get new systems up and running quickly. You’ll push our AI‑assisted development agenda and be a thought leader for the Veho data and engineering community on how to leverage AI in improving development velocity.
You will manage the Machine Learning Operations team and contribute significantly by writing code, reviewing designs, and setting the technical bar. You’ll partner closely with our Agentic Developer Experience and Builder Experience teams.
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
- Drives team velocity by helping the current team develop in their careers, hiring strong new talent onto the team, and adopting AI as a core part of development.
What you’ll do:
- Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work.
- Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated. Think service performance, permissioning, environment setup, and integration with upstream and downstream systems.
- Set the roadmap for improving our Machine Learning and Operations Research infrastructure.
- Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned find their way back into our platform.
- Drive AI usage across DS. Collaborate with our Agentic Developer Experience team to ensure new tooling has a high impact on the Data Science team’s velocity. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science workflows (EDA, model iteration, ML/OR methodologies)
- Be part of the on\-call rotation for our data science production systems.
What You Bring:
- Bachelor’s Degree plus at least 6 years of experience in Machine Learning Engineering, or Master’s Degree plus at least 4 years in Machine Learning Engineering:
- This experience should include:ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelinesexperience managing impactful, high velocity ML Platform / ML Ops teams in smaller scale companiesexperience driving AI/agentic tooling adoption inside an organization
- hands‑on experience with open‑source tooling for large‑scale ML (e.g., Ray, Flink, Feast).
- strong knowledge of Cloud‑based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake).
- Strong proficiency in Python.
- Interest in building systems in a Supply Chain setting, enabling a physical supply chain to run like clockwork.
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: $199K \- $241K
Salary Context
This $199K-$241K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $177K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Veho, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $199K to $241K.
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 Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.