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About This Role
About OIO and Korrus
OIO is the consumer brand from Korrus, a lighting technology company built on more than 500 patents and co\-founded by the Nobel laureate who invented the blue LED. Our products deliver biologically aligned light, the right spectrum at the right time of day, to support how people wake, focus, and rest. Our apps are how customers control that experience and understand what their light is doing for them.
Our apps are live today. They work, and they have taught us a great deal about how people actually use them. We are now rebuilding them on a stronger foundation, and we are looking for the right designer to lead that effort inside a new way of working.
The role
We are looking for a senior contract product designer to lead the design of the cross\-platform mobile and tablet experiences across Korrus apps, beginning with OIO. This is both a UX and a UI role. You will own the flows, the information architecture, and the interface.
What makes this role different is how the work gets made. You will design alongside our engineering team inside AI\-assisted and agentic AI workflows that take ideas to working application faster than a traditional design\-to\-development handoff ever could. We are not looking for someone to produce static design files and pass them downstream. We are looking for a designer who co\-creates the product in real time with engineering and AI, and who is genuinely excited to deepen that practice.
To be clear about what is possible here: the current OIO interface was designed without a dedicated UI designer and without Figma, using AI alone. We are now bringing in design talent to elevate that work and help define how design and engineering build together in an AI\-first environment.
This role reports to our VP, Engineering.
How we see the engagement
We expect this to begin with a focused discovery and audit phase: time for you to learn the products, review how customers use them today, and identify what to keep, what to fix, and what to rethink. From there, we will scope the work together. We are open to your input on the right shape and pace, and we value a designer who would rather scope the work properly than rush to a fixed timeline.
What you'll do
- Design the cross\-platform mobile and tablet user interfaces and experiences across Korrus apps, starting with OIO
- Co\-work directly with engineering inside AI\-assisted and agentic AI workflows to take designs to working application
- Audit the current experiences and identify the gaps, friction points, and structural problems worth solving
- Review usage data and customer feedback, and conduct lightweight research where it sharpens the direction
- Rebuild the information architecture and core flows, from onboarding and device setup through daily use
- Translate the circadian science behind OIO into interfaces that feel simple, calm, and trustworthy
- Apply and extend the existing OIO brand system (the Organic Intelligence palette and visual language) into the apps
- Help shape how design and engineering work together as our AI\-first development practice matures
What we're looking for
- A senior designer with a portfolio of shipped consumer mobile apps, including at least one redesign of a live product
- Genuine comfort with AI tools, and real curiosity about using AI to design and build faster; you want to learn more, not less
- Case studies that show how you diagnosed problems and made decisions, not just the final screens
- Depth in both UX (research, IA, interaction design) and UI (visual design, systems, polish)
- Previous experience with design tools such as Figma; deep mastery is less important than adaptability and a willingness to work in whatever toolchain moves the product forward
- The judgment to balance fixing what's broken with respecting what already works for existing users
- The ability to work independently, scope your own work, and make sound decisions with limited direction
- Experience with UX for iOS/Android apps
Strong pluses
- Experience designing inside AI\-assisted or agentic development workflows
- Experience with health, wellness, sleep, or circadian products
- Experience designing companion apps for consumer hardware, including device pairing and connected\-product flows
Logistics and rate
- Remote, with meaningful overlap with Pacific time zone working hours
- Contract role reporting to the VP, Engineering
- $100 to $140 per hour depending on experience, with the discovery phase scoped as a fixed fee
To be considered
Please share your portfolio along with two to three relevant case studies, ideally including a redesign of a live product. We are especially interested in any work you have done with AI tools in your design process. Finalists may be asked to complete a short paid design exercise so we can see how you work.
Salary Context
This $208K-$291K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Korrus, 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 $180,000 based on 12,397 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($249K) sits 39% above the category median. Disclosed range: $208K to $291K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Korrus AI Hiring
Korrus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $291K - $291K.
Location Context
AI roles in Los Angeles pay a median of $190,500 across 1,764 tracked positions. That's 5% below 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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