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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.
About the Team \- LG's Emerging Technology Lab
LG's Emerging Technology Lab (ETL) is the catalyst for technological innovation within LG's CTO organization. Located in the Silicon Valley and New Jersey, we drive excellence across CTO organizations and business units by pioneering in select emerging technology areas. As the Center of Excellence (CoE), we define and shape key technology domains, setting strategic directions that foster impactful internal and external partnerships to deliver measurable business value.
About the Opportunity
We are seeking a Contract AI Researcher \- Efficient AI to join LG's Emerging Technology Lab in Santa Clara, CA (hybrid). This is an exciting opportunity to work at the forefront of AI efficiency research, developing technologies that make modern LLMs, VLMs, multimodal models, and AI agents faster, smaller, and more deployable in real\-world environments.
In this role, you will explore cutting\-edge areas such as model compression, quantization, efficient inference, reasoning optimization, and next\-generation AI architectures. Your work will help enable advanced AI capabilities across LG's future products and platforms, including AI PCs, edge devices, robotics, and intelligent vehicle systems.
The ideal candidate enjoys bridging research and implementation, transforming ideas from the latest scientific literature into working prototypes and measurable improvements. You will have the opportunity to collaborate with experienced researchers, contribute to publications and intellectual property, and help shape the future of efficient, on\-device AI.
Responsibilities
- Research, prototype, and implement AI methods that improve model efficiency, inference performance, and deployment feasibility on constrained devices.
- Optimize modern LLMs, SLMs, VLMs, multimodal models, and agentic workloads across post\-training, inference, and deployment workflows.
- Propose and evaluate novel compression methods (PTQ, QAT, pruning, low\-rank approximation, etc) for on\-device LLM/VLM enablement.
- Devise approaches to address challenges related to long\-context inference and KV cache compression in the context of reasoning and agentic applications.
- Develop gradient\-free and backpropagation\-free methods for model merging, compression, and efficiency\-driven optimization.
- Implement and evaluate emerging efficient architectures and modules, including MoE, SSMs, hybrid models, Looped Transformers, etc.
- Prototype inference\-time optimization methods such as speculative decoding, constrained decoding, low\-latency generation, and kernel\-level optimization.
- Build experimental pipelines, perform evaluations on standardized language, vision, reasoning, and agentic benchmarks.
- Contribute to publications, technical reports, open\-source releases, invention disclosures, and IP submissions where appropriate.
Required Qualifications
- M.S. or Ph.D. in Computer Science, Computer Engineering, Machine Learning, Mathematics, or a related technical field. Relevant post\-graduate research and/or industry experience is preferred but not required.
- Research or engineering experience in ML, efficient AI, model optimization, or AI systems.
- Strong programming ability in Python and experience with PyTorch or a comparable deep learning framework.
- Hands\-on experience with modern LLMs, SLMs, VLMs, multimodal models, or generative AI systems.
- Ability to read research papers, implement technical methods, run experiments, and communicate results clearly.
- Comfortable working in a fast\-moving and ambiguous technical environment.
- Strong written and verbal communication skills for reports, presentations, demos, and technical documentation.
Ways to Stand Out
- Publications in reputable venues in ML and/or systems space (e.g., ICML, ICLR, NeurIPS, ACL, COLM, EMNLP, MLSys, MICRO, etc).
- Experience with modern LLM/VLM inference and deployment frameworks such as llama.cpp, GGUF, vLLM, SGLang, TensorRT\-LLM, or related systems.
- Experience with efficiency\-aware post\-training or finetuning methods such as PTQ, QAT, LoRA, distillation, instruction tuning, DPO, OPD, RLVR, or reasoning\-oriented adaptation.
- Experience with low\-level kernel implementations and on\-device acceleration.
- Familiarity with emerging architectures such as MoE, SSMs, hybrid attention, or Looped Transformers.
- Experience with AI\-assisted optimization, multi\-agent systems, or agentic\-based workflows for Efficient AI and hardware/software co\-design.
Contract: This is expected to be a one\-year contract position, with the potential for extension based on business needs and performance.
Third\-Party Agency Notice: We are not accepting unsolicited resumes or candidate submission from staffing agencies or search firms for this position. Please do not contact us regarding this opportunity.
\#LI\-JH1 \#Hybrid
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 [email protected]. This email and phone number will only reply to accommodation requests and is not intended for general employment inquiries.
All qualified applicants will be considered for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.
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 $174K-$189K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($182K) sits 15% below the category median. Disclosed range: $174K to $189K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LG Electronics AI Hiring
LG Electronics has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Santa Clara, CA, US, San Francisco, CA, US, New York, NY, US. Compensation range: $189K - $210K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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