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About This Role
About TESO Group
Founded in 2017 in New York, TESO Group is an Asian-inspired lifestyle retail company bringing premium Asian products and culture to the U.S. market. With brands including TESO LIFE, TESO X, and TESO Mini, we offer a curated mix of beauty, snacks, home goods, and trendy collectibles—delivering quality, value, and a unique shopping experience.
Position Overview
This engineer guards both the company's defenses and its compliance cadence. They build and operate the security toolchain while managing the PCI calendar, access reviews, and evidence. As AI tools become part of everyday work, this role also secures the company's AI adoption and uses AI to make security operations faster and more consistent.
This role does not set company-level security or AI strategy, which is owned by the Digital Information Manager. Instead, it lands controls onto systems, endpoints, and AI tools, and keeps compliance continuously attained.
Job Responsibilities
1.Security Toolchain
- Build and operate EDR, email security, identity security, and logging; land MFA and 802.1X.
- Run vulnerability scans and remediation; manage store and cloud baselines.
- Use AI-enabled capabilities in the toolchain, such as alert triage, anomaly detection, phishing/BEC detection, and natural-language log queries, to reduce noise and speed up response.
2. AI Security & Governance
- Implement controls for approved AI tools (e.g., enterprise AI workspaces): SSO/MFA, role-based access, data retention settings, and integration with DLP/SIEM through compliance and audit APIs.
- Detect and manage unapproved "shadow AI" use. Prevent sensitive data, including cardholder, customer, and financial data, from being shared with unapproved AI services.
- Review the security of new AI tools, connectors, and integrations before rollout. This covers data handling, permissions scope, vendor security documentation, and risks such as prompt injection and over-permissioned access to email and file stores.
- Help operationalize the company's AI acceptable-use policy and deliver employee awareness on AI-related threats, such as AI-generated phishing and deepfake voice/video impersonation.
3.PCI Compliance
- Run quarterly scans and the annual assessment, prepare the SAQ/ROC, and drive remediation to closure.
- Maintain the evidence repository and support the QSA and external audits.
- Ensure AI tools and workflows are kept out of PCI scope, or properly controlled where they touch in-scope systems. Use automation and AI assistance to streamline evidence collection and control mapping.
4.Access reviews
- Review business-system, cloud, and MSP access periodically.
- Manage third-party access onboarding, approval, and revocation, including warehouse-vendor remote access.
- Include AI tool access, AI service accounts, API keys, and AI integration permissions in periodic access reviews.
5.Incidents & reviews
- Investigate, contain, and review security incidents, including incidents involving AI tools or AI-enabled attacks.
- Provide security review for infrastructure and system changes, including new AI features and integrations. Qualifications
- Education: Bachelor’s degree or above; cybersecurity or computer science preferred.
- Experience: 4+ years in security engineering or operations; PCI or GRC experience preferred. Hands-on experience securing or governing enterprise AI tools is a plus.
- Knowledge & Skills: EDR, SIEM, and vulnerability tooling; PCI-DSS controls and IAM concepts; Python scripting.
- AI Knowledge: Working knowledge of AI/LLM security risks, such as data leakage, prompt injection, and excessive permissions; familiarity with frameworks like the NIST AI RMF or OWASP Top 10 for LLM Applications is a plus. Comfortable using AI assistants to accelerate scripting, investigation, and documentation, while validating AI output before acting on it.
- Competencies: Fast responder with strong evidence discipline; principled; switches fluently between technical and compliance work. Curious and pragmatic about new technology, enabling safe AI adoption rather than simply blocking it.
Compensation* $100,000 – $150,000 per year
- Quarterly Performance Bonus: This position is eligible for a quarterly performance-based bonus, subject to the eligibility requirements, performance criteria, and terms of the Company's bonus program.
Benefits* 401(k) & 401(k) matching - subject to the eligibility requirements
- Paid Sick Leave
- Employee discount
- Health insurance
- Paid Time Off
Job Type: Full-Time (On-Site)
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Teso Life, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Teso Life AI Hiring
Teso Life has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Flushing, NY, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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