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About This Role
Step into the innovative world of LG Electronics. As a global leader in technology, LG Electronics is dedicated to creating innovative solutions for a better life. Our brand promise, 'Life's Good', embodies our commitment to ensuring a happier life for all. We have a rich history spanning over six decades and a global presence in over 290 locations. Our diverse portfolio includes Home Appliance Solutions, Media Entertainment Solutions, Vehicle Solutions, and Eco Solutions. Our management philosophy, "Jeong\-do Management," embodies our commitment to high ethical standards and transparent operations. Grounded in the principles of 'Customer\-Value Creation' and 'People\-Oriented Management', these values shape our corporate culture, fostering creativity, diversity, and integrity. At LG, we believe in the power of collective wisdom through an inclusive work environment. Join us and become a part of a company that is shaping the future of technology. At LG, we strive to make Life Good for Everyone.
What we can offer*:*
*A crafted employee experience designed to foster professional growth, a focus on health \& well\-being and an internal community that will set you up for success.*
We offer an environment that enables colleagues to demonstrate their capabilities, focus on their work and create value. At LG, you are encouraged to take a creative and individual approach to challenges with strong emphasis placed on performance and skill—and equal, merit\-based opportunities across the board. We want our colleagues to grow with our global business. That's why we deliver sure rewards for exceptional performance and offer industry\-leading benefits. Come join the team!
Position Overview
As the Senior Director of Supply Chain Management \- Logistics, you will be responsible for developing and implementing LG’s last mile logistics strategy for the American market. With the recent organizational restructuring, this role is vital for stabilizing our transportation and hub operation functions. Your focus will be on transforming the customer delivery experience by ensuring reliability for key partners (such as builders and retail customers) and end consumers. You will take immediate action to resolve last mile operational issues, enforce capacity management processes, enhance on\-time delivery performance and installation quality, and optimize a network handling high\-volume, bulky/white\-glove appliances.
Following the successful establishment of LG Dedicated LMD Hubs in 2025, LG Electronics is entering a crucial phase of Operational Optimization and Internalization. The emphasis is shifting from "Network Construction" to "Operational Optimization," specifically targeting cost competitiveness and quality control. The Senior Director of Last Mile Delivery will advance beyond simple network management to execute a "LG\-led Dedicated LMD Network" strategy. You will leverage your expertise to integrate siloed volumes (OBS, Builder, LTL) to optimize truck density, centralize dispatch operations, and maintain direct oversight of hub operations to eliminate service discrepancies found in our "Shared" network models.
Key Responsibilities
- Strategic Network Management \& Improvement
+ Service Stabilization:
- Quickly assess and address service level gaps in key regions to eliminate operational failures. Lead the transition of regions serviced by underperforming shared network providers to LG Dedicated Hubs or high\-performance partners.
- Network Design: Optimize the Last Mile carrier mix, reducing reliance on single\-source failures and diversifying the Last Mile Delivery network to ensure capacity during peak seasons.
- Cost \& Quality Balance: Strive for cost efficiency while maintaining the "White Glove" service standards essential for premium home appliances.
+ Vendor Compliance \& Carrier Governance
- Strict Oversight: Enforce stringent contract compliance for the use of third\-party subcontractors.
- Implement audit mechanisms to ensure unauthorized lower\-tier carriers are not utilized without LG’s explicit approval.
- Performance Scorecards: Revise QBRs and MBRs with strict KPIs centered on On\-Time Delivery (OTD), Damage\-Free Delivery, and Customer Communication metrics.
- Contract Negotiation: Lead negotiations with logistics service providers (LSPs) to secure service guarantees and control clauses for non\-performance.
+ Key Account Operations
- Capacity Planning: Act as the primary logistics point of contact for significant operational escalations with strategic partners (National Builders, Major Retailers). Lead collaborative planning and forecasting processes with Sales and Product Management to ensure capacity for regular and peak seasons, managing temporary fleet expansions when volumes exceed forecasts.
- Corrective Action Plans: Proactively develop and present CAPs (Corrective Action Plans) to retail leadership to build trust and demonstrate systematic improvements in delivery reliability.
- Integration: Collaborate closely with retailer supply chain teams to integrate data flows, ensuring real\-time visibility of inventory status from warehouse to doorstep.
- Technology \& Visibility
+ Data Transparency: Advocate for the implementation and optimization of visibility platforms (such as Project 44 or similar integrations with existing ERP/TMS) to offer real\-time tracking to customers.
+ Analytics: Use data to anticipate delivery bottlenecks before they lead to missed appointments or operational failures.
- KPI Improvement
+ Oversee the Hub Setup Dashboard and KPI metrics, specifically tracking On\-Time Delivery (OTD), Return Rate, and NPS (Net Promoter Score).
Qualifications
- A successful background with 15\+ years in Supply Chain/Logistics, including at least 5 years in a senior leadership role managing Last Mile operations for bulky goods (appliances/furniture) or retail.
- Crisis Management: Proven history of revamping underperforming logistics networks and managing high\-stakes retailer escalations.
- Vendor Management: Experience managing 3PLs/4PLs, with strict contract enforcement and carrier development. Proven background in implementing audit mechanisms to ensure no unauthorized lower\-tier carriers are used without LG’s explicit approval.
- Performance Scorecards: Ability to enhance QBRs and MBRs with clear KPIs focused on On\-Time Delivery (OTD), Damage\-Free Delivery, and Customer Communication metrics.
- Contract Negotiation: Strong skills in leading negotiations with logistics service providers. Proven ability to transform underperforming logistics networks and handle high\-stakes retailer escalations.
- Technical Proficiency: Knowledge of TMS, ERP (SAP/Oracle), visibility tools, routing optimization, and analytics platforms.
- Educational Background: A bachelor's degree in supply chain, Engineering, or Business; an MBA is preferred.
- Certifications: APICS CPIM/CSCP, ISM CPSM, or equivalent certifications are a plus.
Key Competencies
- Executive presence with the ability to present data and action plans to internal executives and major customers.
- Capability for crisis management and resilience in high\-pressure operational environments.
- Strong leadership in driving large\-scale operational change and network transformation.
- High analytical capability, emphasizing forecasting, visibility, and predictive decision\-making.
Ability to Drive Four Key Strategic Missions:
- Volume Integration \& Density Optimization (Cost Strategy)
+ Mission: Execute the "Integrated LMD X\-Dock" strategy by consolidating distinct volume streams—Online Brand Shop (OBS), Direct Builder, and LTL \- into a single delivery schedule.
+ Target: Increase truck productivity by 30% (productivity – stops per vehicle).
+ Financial Goal: Reduce total LMD operating costs by 20% through optimized routing and load factors.
- Centralization of Dispatch \& Routing (Control Strategy)
+ Mission: Transition from manual, hub\-level dispatching to a Centralized Dispatch Control Tower.
+ Action: Implement automated routing optimization tools (TMS Load Planning) to centrally manage all dedicated hubs, ensuring consistent route efficiency and reducing reliance on local hub capabilities.
- Direct Hub Management \& Quality Standardization (Quality Strategy)
+ Mission: Shift from a passive 3PL reliance model to Direct Hub Management, insourcing "Operation Managers" at key hubs to oversee Business Partners Operators.
+ Action: Establish a "Master\-Level" Installation Quality Certification program and enforce strict SOPs to stabilize installation quality across the network.
- B2B \& Builder Business Expansion (Growth Strategy)
+ Mission: Operationalize National Direct Builder Contract and enhance support for major builders.
+ Action: Implement strict control protocols and pre\-installation checks to reduce wasted trips and improve "First Available Date" (FAD) reliability for high\-volume builder accounts.
Recruiting Range
$207,000—$304,000 USD
Benefits Offered Full\-Time Employees:
- No\-cost employee premiums for you and your eligible dependents for competitive medical, dental, vision and prescription benefits.
- Auto enrollment with immediate vesting of competitive company matching contributions in a 401(k) Retirement Savings Plan with several investment options.
- Generous Paid Time Off program that includes company holidays and a combined bank of paid sick and vacation time.
- Performance based Short\-Term Incentives (varies by role).
- Access to confidential mental health resources to help you and your loved ones improve your quality of life. Personal fitness goal incentives.
- Family orientated benefits such as paid parental leave and support for families raising children with learning, social, behavioral challenges, or developmental disabilities.
- Group Rate Life and Disability Insurance.
Benefits Offered Temporary/Contractors:
- Eligible for the relevant benefit programs offered through our partner agencies.
Privacy Notice to California Applicants
Applicants who need assistance or a reasonable accommodation during the hiring process may contact our team by phone at: 973\-477\-7090 or support@lg4me.freshdesk.com. This email and phone number will only reply to accommodation requests and is not intended for general employment inquiries.
At LG, we aspire to empower people and celebrate differences because we believe diversity will create the unexpected. We provide equal employment opportunity to all individuals regardless of their race, color, creed, religion, gender, age, sexual orientation, national origin, disability, veteran status, or any other characteristic protected by state, federal, or local law. Consistent with our commitment to providing equal opportunity and embracing diversity, LG has implemented affirmative action to ensure applicants are employed and employees are treated without regard to these characteristics.
In addition to the above, LG believes that pay transparency is a key part of diversity, equity, and inclusion. Our salary ranges take into account many factors in making compensation decisions including but not limited to skillset, experience, licensure, certifications, internal equity, and other business needs. While we consider geographic pay differentials in final offers, because we operate in many geographies where applicable, the salary range listed may not reflect all geographic differentials applied*.*
Salary Context
This $207K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At LG Electronics, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($255K) sits 53% above the category median. Disclosed range: $207K to $304K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
LG Electronics AI Hiring
LG Electronics has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Englewood Cliffs, NJ, US, Alpharetta, GA, US, Washington, DC, US. Compensation range: $58K - $304K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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