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Qualifications
- Builds the working system for each engagement phase. Ingests footage, curates and labels data, trains and evaluates models on the platform, deploys inference to the edge or cloud, and iterates with the customer until the output is trusted.
- Works fluently in the platforms' SDKs, APIs and workflow tools. Knows when the no / low-code path is enough and when to drop to code. Contributes reusable connectors, scripts and workflow templates back to the team.
- Connects to the customer's existing camera estate over RTSP or ONVIF and through their video management system. Integrates detections and events with the systems operators already use.
- Builds the alert logic, dashboards and reports that turn model output into something a line supervisor or field operations lead acts on. Tunes thresholds with the people who receive the alerts.
- Packages and deploys models to NVIDIA Jetson, on-prem GPU servers or cloud endpoints, including containerization, ONNX or TensorRT conversion, monitoring and rollback.
- Spends extended time at customer facilities during prototype and pilot phases. Understands the floor, learns the process, and adjusts camera placement, zones and event definitions with the operations team.
- Owns the retraining loop during the pilot. Diagnoses errors, pulls hard examples back into the training set, and documents the retraining triggers the customer inherits.
- Delivers runbooks, architecture notes, deployment scripts and a monitoring setup so the customer or WWT managed services can operate the system independently.
- Runs the hands-on portion of platform evaluations with the Solution Architect and product leadership, standing up the trial, loading the test dataset, executing the scoring scenarios and recording what broke.
- Builds and maintains demo environments and reference builds in the ATC and AI Proving Ground that sales and delivery teams can reuse.
- Extends the same build pattern into digital twin, simulation and robotics engagements, including synthetic data, simulation environments and robot or AMR integration alongside partners.
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $126,000 to $158,000 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that are not included in the base pay.
The well-being of WWT employees is essential. When it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full-time employees:
- Health and Wellbeing: Health (Medical & Prescription), Dental, and Vision Care, Onsite Health Centers (MO & IL), Employee Assistance Program, Wellness program
- Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement
- Paid Time Off: PTO & Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring
- Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program
Note: This is not an all-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US benefits website at wwt.com/us-benefits.
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for all!
If you require accessibility accommodation(s) or adjustment during any stage of the hiring process, please let your WWT Recruiter know. The recruiter will work with you to understand your needs and help ensure an accessible experience throughout the interview process.
World Wide Technology is an Equal Opportunity Employer.
Requirements: Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
Founded in 1990, WWT brings together strategy, deep technical expertise and world-class partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state-of-the-art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distributions capabilities.
With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.
Want to work with highly motivated individuals on high-performance teams? Join WWT today!
Job Summary
The Forward Deployed Specialist is the builder on the ground for WWT's Physical AI engagements, from the first prototype through a pilot running in the customer's environment. Where the Solution Architect owns the design, this role makes it work on real cameras and sensors, in a real plant or substation or stadium, with real operators watching the output. Perception comes first in Physical AI. Everything downstream, from an alert to a digital twin to a robot, is only as good as the visual data it receives, and the engineer is the person who finds out on day one whether the cameras can actually see what the use case needs.
The specialist works primarily on enterprise vision AI platforms, the kind that cover data curation, labeling, training, deployment and monitoring in one tool, using their SDKs and APIs when the engagement needs custom work and their low-code interfaces when it does not. The job is to wire the platform into the customer's cameras, sensors and existing systems, get a model trained and deployed on the customer's data, build the alerting and reporting that operations teams will actually use, and hand the result off to production with documentation someone else can run from. As the team grows, the same seat extends into digital twin, simulation and robotics engagements.
This is a hands-on builder role for someone comfortable prototyping a working example of an idea generated days earlier and staying with it until it runs in production. The raw material is video, sensor data, edge hardware and the operational systems a facility already runs on.
Job Responsibilities
- Builds the working system for each engagement phase. Ingests footage, curates and labels data, trains and evaluates models on the platform, deploys inference to the edge or cloud, and iterates with the customer until the output is trusted.
- Works fluently in the platforms' SDKs, APIs and workflow tools. Knows when the no / low-code path is enough and when to drop to code. Contributes reusable connectors, scripts and workflow templates back to the team.
- Connects to the customer's existing camera estate over RTSP or ONVIF and through their video management system. Integrates detections and events with the systems operators already use.
- Builds the alert logic, dashboards and reports that turn model output into something a line supervisor or field operations lead acts on. Tunes thresholds with the people who receive the alerts.
- Packages and deploys models to NVIDIA Jetson, on-prem GPU servers or cloud endpoints, including containerization, ONNX or TensorRT conversion, monitoring and rollback.
- Spends extended time at customer facilities during prototype and pilot phases. Understands the floor, learns the process, and adjusts camera placement, zones and event definitions with the operations team.
- Owns the retraining loop during the pilot. Diagnoses errors, pulls hard examples back into the training set, and documents the retraining triggers the customer inherits.
- Delivers runbooks, architecture notes, deployment scripts and a monitoring setup so the customer or WWT managed services can operate the system independently.
- Runs the hands-on portion of platform evaluations with the Solution Architect and product leadership, standing up the trial, loading the test dataset, executing the scoring scenarios and recording what broke.
- Builds and maintains demo environments and reference builds in the ATC and AI Proving Ground that sales and delivery teams can reuse.
- Extends the same build pattern into digital twin, simulation and robotics engagements, including synthetic data, simulation environments and robot or AMR integration alongside partners.
Salary Context
This $126K-$158K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At World Wide Technology, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $126K to $158K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
World Wide Technology AI Hiring
World Wide Technology has 15 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, Remote, US, St. Louis, MO, US. Compensation range: $92K - $219K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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