Interested in this AI/ML Engineer role at LG Electronics?
Apply Now →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.
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, distribution, and government relationships behind them.
We are currently recruiting for our next Founder in Residence cohort, with the first 90\-day validation sprint targeted to begin on October 1, 2027\.
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 are domain agnostic, we are focused on AI\-enabled software ventures with strong commercial potential
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.
NOTE: We are building software solutions only, so no hardware, no robotics, and nothing capital intensive upfront. Every venture in this cohort is AI\-native. The industry is yours to pick. For this cohort we are not considering health or clean tech (other verticals at LG NOVA), so if that is your space, this is not the cohort for it.
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 experience raising funding rounds. 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 leverage.
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.
A concept testing budget so you can put real money behind proving demand
A de\-risking idea to commercialization budget up to 500K, setting you up for Seed round.
Government partnerships in multiple U.S. 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
A competitive stipend at market rates while you do the work
Equity in the venture you just validated and commercialized if it progresses to the 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.
Sprint 2 is 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
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 hybrid role based in Santa Clara, California.
The Founder in Residence program is designed as two consecutive 90\-day sprints. Compensation is annualized and paid during each sprint as follows:
- Sprint 1 (90 days): Annualized compensation equivalent of $180,000–$200,000, plus a dedicated concept testing budget.
- Sprint 2 (90 days): Annualized compensation equivalent of $200,000–$240,000, contingent upon successful completion of Sprint 1 and selection to continue into the commercialization phase.
We are selecting a very small, highly selective founding cohort for this round, so the bar is high and the opportunity is significant.
If your venture is successfully spun out and you transition into the role of Founder \& CEO, your executive compensation package—including salary, equity, and other compensation elements—will be negotiated separately as part of the new company's formation and financing.
How to apply
Send us
- Your resume
- A short founder summary, about one page, answering the three questions below in a few sentences each:
1\) What experience have you had building? 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?
2\) What traction did you have? Tell us about a time you found a real customer need, proved demand, and won early customers. What did you do, and what happened?
3\) 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
Recruiting Range
$180,000—$200,000 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 [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 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 in Demand for This Role
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 ($190K) sits 12% below the category median. Disclosed range: $180K to $200K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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