Interested in this AI/ML Engineer role at LG Electronics?
Apply Now →Skills & Technologies
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.
Founder in Residence, AI Ventures - Core AI
LG NOVA, LG Electronics North America Innovation Center
You are not applying for a job. You are here to found LG's next company.
LG NOVA builds new ventures and spins them out as real businesses. We are looking for a small group of founders to validate and launch the next one, with LG's balance sheet and government relationships behind them. We are recruiting now for our next Founder in Residence cohort, with the first 90 day validation sprint targeted to begin on December 1, 2026.
This is a founding seat, not an advisory one. If the venture proves out, you lead it as Founder and CEO.
The mandate is open on purpose
We are not handing you a problem to solve. The strongest ventures come from founders who already see something the market has missed, so we are domain agnostic by design. While we stay open on the domain, we are focused on AI enabled software ventures with strong commercial potential.
We are looking for candidates with experience building businesses at the core AI layer or adjacent products/tools, rather than mainly building applications using AI. The strongest candidates have worked directly on the AI lifecycle, including model training or fine tuning, evaluation, deployment, inference, optimization, and production scale operation. Relevant areas of business may include:
- AI Management, Development Platforms
- Agentic Coding, Dev Automation
- Agent Orchestration, Workflow Automation
- AI Safety, Security, Compliance
- Data Infrastructure, Data Generation, Curation
Relevant experience may include model training infrastructure, inference and serving, model optimization, AI runtimes, agent infrastructure, orchestration, evaluation, observability, governance, data infrastructure, or AI native developer platforms. You should understand model and data pipelines, compute and performance trade offs, production reliability, and the economics of running AI systems at scale.
Strong preference goes to candidates who have built reusable AI infrastructure, platforms, or developer tooling that many applications or enterprise customers can build on, and who can turn technical capability into a differentiated, commercially viable B2B venture.
Bring the problem you have unfair insight into, the buyers you already know, and the conviction that it should exist as a company. Every venture in this cohort is AI native.
We are building software only, so no hardware, and nothing capital intensive to get off the ground. The industry is yours to pick, but for this cohort we are not considering health or clean tech, which sit with other teams at LG NOVA, so if that is your space this is not the cohort for it.
This is not an accelerator. We are looking for founders who want to build and validate a new venture from the ground up. If you already have a company or a product you are trying to push forward, this is not the right fit.
Who we are looking for
You are a domain operator with deep access to the buyers in your space. You have built or commercialized something real and have raised funding before. People in your industry take your call. Maybe you founded before, maybe you raised before, maybe you ran a business line that behaved like a startup. You move fast when the path is unclear, and you close.
The profile we want
- Founder, repeat founder, or founding executive experience
- Deep expertise and credibility in a specific industry or problem space
- A live network of buyers and decision makers in your domain that you can call on
- A track record of building, commercializing, or scaling something new
- Comfort in ambiguity with a bias toward evidence and revenue
- A clear view on why AI makes your venture buildable now and not three years ago
What LG NOVA offers
You get resources most early founders would fight for.
- Competitive compensation at market rates while you do the work
- A concept testing budget so you can put real money behind proving demand
- A budget of up to $500K to carry a validated idea toward commercialization, setting you up for a Seed round
- Government partnerships across multiple US states that open real doors to pilots, design partners, and first customers
- An LG backed fund that gives validated ventures a direct path to capital, so you are not starting your raise from zero
- Market research, market sizing, and financial modeling done alongside you, not billed back to you
- Engineering services to help you test and build
- Equity in the venture you validated and commercialized if it progresses to a Seed round
You validate. We resource it.
How the program runs
The program runs in two sprints.
Sprint 1 is 90 days of validation. You define a specific high value problem, identify the economic buyer, test willingness to pay, and generate hard evidence that a venture should exist. The point is proof, not a product.
We run this in the open with you. You meet with us weekly to review progress and evidence, and at set checkpoints around Day 30, 45, and 60 we decide together whether to sharpen the concept, redirect it, or stop it. Redirecting or stopping a concept early is part of how this works, not a mark against you.
Sprint 1 ends with a panel review on Day 90. That panel decides whether the venture continues into Sprint 2.
Sprint 2 is around 180 days of building toward venture formation. You move from evidence to commercialization, early customers, and the foundation of a company. If the venture earns it, you become Founder and CEO of a new business built with LG behind you.
What you will do
- Develop and sharpen a venture thesis in a space you know cold
- Run buyer discovery with the people who actually hold the budget
- Pin down the economic buyer, the budget owner, and how they buy
- Validate the pain, the workflow, and the real size of the demand
- Design and test a paid pilot, a design partner program, or an early offering
- Secure concrete evidence of willingness to pay, whether a paid pilot, a signed letter of intent with commercial terms, or a design partner agreement, from a named buyer
- Build the commercialization plan and the business model
- Bring back a recommendation grounded in what the market told you
What success looks like by the end
- A clearly defined venture opportunity
- A validated customer and economic buyer
- Hard evidence of demand
- A credible path to revenue
- Signed design partners, pilots, or customer commitments
- A commercialization plan that scales
- Enough conviction that LG should invest and build
The details
This is a contract, hybrid role based in Santa Clara, California, structured as two consecutive sprints. It is a full-time, exclusive engagement. While you are in the program you are not building, advising, or holding an active role in another startup, including as a passive or part time founder. Your focus is this venture and nothing else.
Compensation is annualized and paid during each sprint.
Sprint 1, the first 90 days, pays an annualized equivalent of $180,000 to $200,000, plus a dedicated concept testing budget.
Sprint 2, around 180 days, pays an annualized equivalent of $200,000 to $240,000, contingent on completing Sprint 1 and being selected to continue into commercialization.
The ventures built here are LG ventures. The work and IP you create during the program belong to LG NOVA, and in return you share in the upside through equity if the venture progresses to a Seed round, and the path to Founder and CEO if it spins out.
We are selecting a very small, highly selective founding cohort for this round, so the bar is high and the opportunity is real. If your venture spins out and you step into Founder and CEO, your executive package, including salary, equity, and other terms, is negotiated separately as part of forming and financing the new company.
How to apply
Send us
- Your resume
- A short founder summary, about one page, answering the three questions below in a few sentences each
What did you build? Pick one venture, product, or business you personally helped build or commercialize. What was the problem, what did you own, and what were the measurable results such as revenue, customers, funding, or adoption?
Which buyers do you know? Tell us about a time you found a real customer need, proved demand, and won early customers. What did you do, and what happened?
How do you move? Give us one example of using your network to accelerate something, a key hire, a partnership, a customer relationship, or an industry connection that changed the outcome.
#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 $180K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $180K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
LG Electronics AI Hiring
LG Electronics has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Mountain View, CA, US, Santa Clara, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.