Interested in this AI/ML Engineer role at Toshiba Global Commerce Solutions?
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About This Role
Toshiba Global Commerce Solutions is seeking a Platform Performance & Resilience (AI‑Enabled) Engineer that is a hands-on architecture role responsible for leading performance, scalability, and resilience engineering across a distributed platform spanning edge devices, in‑store servers, and cloud services. Sitting at the intersection of customer delivery, platform architecture, and production engineering, this role engages directly with customers and internal teams to define performance expectations, validate real‑world system behavior, drive performance testing strategies, and leverage AI to scale non-functional validation across the platform.
Note: Toshiba Global Commerce Solutions will not be providing visa sponsorship for this position now or in the future. Therefore, in order to be considered for this position, you must have the ability to work in the United States without a need for current or future visa sponsorship.
Must be located near or within the Raleigh-Durham area.
Responsibilities
Customer-Facing Performance & Scalability Engineering
- Engage directly with customers to define performance expectations, scale targets, and operational constraints.
- Translate business requirements into concrete performance models and test plans.
- Provide guidance on system behavior under peak loads, store concurrency, and transaction volume.
Performance Testing Strategy (k6 and Beyond)
- Define and lead platform-wide performance testing using k6.
- Design realistic load models reflecting POS behavior, store concurrency, and cloud interactions.
- Ensure repeatable, automated performance validation integrated into delivery pipelines.
Virtualization & Simulation of Performance Environments
- Lead the design of virtualized environments simulating production-scale behavior.
- Build and evolve simulators for store workloads, transaction flows, and external dependencies.
- Enable testing at scale without requiring full production infrastructure.
Infrastructure Sizing & Capacity Modeling
- Own infrastructure sizing models used in customer Statements of Work (SOWs).
- Provide validated recommendations for cluster sizing, database capacity, message throughput, and resource allocation.
Resilience Architecture & System Behavior
- Define system behavior under failures, degraded dependencies, and partial outages.
- Establish patterns such as timeouts, retries, circuit breakers, graceful degradation, and workload isolation.
Observability & Performance Insight
- Ensure end-to-end transaction visibility across edge, store, and cloud.
- Define telemetry requirements to quickly identify performance bottlenecks.
- Improve signal quality for performance and reliability diagnostics.
AI-Enabled Validation
- Use AI-driven systems to generate and evaluate performance and resilience scenarios.
- Improve detection of regressions and anomalies in large-scale test runs.
- Automate validation workflows to enhance production readiness.
Required Qualifications
- 5 + years of software engineering experience with a focus on distributed systems.
- Proven experience in performance engineering and scalability architecture.
- Hands-on experience with k6 or similar performance testing tools.
- Experience designing and executing load, stress, and scalability tests.
- Strong background in capacity planning and infrastructure sizing.
- Experience working directly with customers on performance and system behavior.
- Deep understanding of distributed systems patterns: concurrency control, latency management, retries/backoff, circuit breakers.
- Experience with cloud-native systems (AWS, GCP, or Azure) and Kubernetes.
- Strong debugging and performance analysis skills.
Preferred Qualifications
- Experience with multi-tier architectures (edge, on-prem/store, cloud).
- Experience building simulation or virtualization environments.
- Familiarity with event-driven systems such as Kafka, Pub/Sub, or MQ.
- Experience implementing observability frameworks (OpenTelemetry, Grafana, ELK).
- Exposure to AI-driven testing or validation systems.
Toshiba Global Commerce Solutions is a dynamic billion-dollar global company based in Research Triangle Park, NC, providing retail store solutions to your favorite brands. Have you ever been in a hurry and made use of the self-checkout at Lowe's Foods, earned fuel rewards at Kroger, or just paid for purchases at retailers such as Walmart, Michaels, Carrefour, The Gap, Calvin Klein, Boots, Cencosud, BJ's, or Costco? These are just a few examples of our in-store solutions and impressive customer base that made us the world's installed market share leader.
The nature of retail is changing quickly, so if you share our 'Together Commerce' vision of a seamless two-way, participatory shopping experience, let's get together to drive the new economy.
Toshiba Global Commerce Solutions, Inc. offers a competitive salary and generous benefits package including the following:
- Group health coverage (medical, dental, & vision)
- Employee Assistance Programs
- Pre-tax spending accounts
- 401(k) plan (with company match)
- Company provided life insurance
- Pet Insurance
- Employee discounts
- Generous paid holiday schedule, paid vacation & sick/personal days
EEO:
Toshiba Global Commerce Solutions is an equal opportunity/affirmative action employer that evaluates qualified applicants without regard to age, ancestry, color, religious creed, disability, marital status, medical condition, genetic information, military or veteran status, national origin, race, sex, gender, gender identity, gender expression and sexual orientation or any other protected factor. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
Individuals who need a reasonable accommodation because of a disability for any part of the employment process should email [email protected] to request an accommodation
DIVERSITY, EQUITY & INCLUSION:
We at Toshiba Global Commerce Solutions firmly believe that our people are an integral part to the success of our customers. Furthermore, we're committed to Diversity, Equity, and Inclusion for all our people as highlighted by our 5 Core Principles (Create Outreach, Foster Belonging, Unleash Opportunity, Diverse Cultural Engagement and Culture of Transparency). We're passionate about our customers the retail industry and becoming a more responsible company as we help create a brighter future.
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 Toshiba Global Commerce Solutions, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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.
Toshiba Global Commerce Solutions AI Hiring
Toshiba Global Commerce Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Durham, NC, US, Durham, NC.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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