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About This Role
About Within
Tldr on us: Series B, $91M raised, 7x growth last year.
Digital Transformation gave rise to a $600B consulting Industry. AI Transformation will be 10x bigger and will be delivered through agents. We built that.
Our AI discovers how work actually happens across every team and application, Structures it into a living Context Graph, and Improves it continuously.
ServiceNow mapped 900+ processes in 9 days. DoorDash captured 3,800+ finance operations in 14 weeks. That's not a project. That's compounding intelligence.
OpenAI, Google, DoorDash, and Stripe use Within to transform how they transform. We shipped GPT-4 document chat within 12 hours of OpenAI's API launch.
We move fast, reward agency, and care deeply about our customers, our team, and our mission.
The Role
We are looking for an AI Front-end Engineer who sits at the intersection of product design and frontend engineering. You are an engineer first, but with exceptional design sensibility that elevates everything you build. You will be responsible for defining and implementing the "look and feel" of AI-native workflows, ensuring that our complex enterprise tools feel as intuitive as the best consumer apps.
Note: We do not require a CS or Engineering degree. We care about your portfolio, your GitHub (or equivalent), and your track record of shipping.
Who You Are
- Engineer first, designer by instinct - You're a front-end engineer with a natural eye for great design. You can discuss the tradeoffs of Server Components versus Client Components just as fluently as you can critique spacing and visual hierarchy.
- Expert in modern web technologies - React, Next.js, TypeScript, and Tailwind aren't just in your toolkit—you've truly mastered them. You understand the performance consequences of your architectural decisions and ship code that's both elegant and performant.
- AI-native developer with creative problem-solving chops - You wield tools like Cursor, Claude Code, v0.dev, and Lovable as extensions of your craft. You use AI to prototype 10 variations in the time it used to take to build one, letting you focus on the hard problems: information architecture, user flows, and delightful interactions.
- Ship with velocity and craft - You find the shortest path to get features in front of users while obsessing over the details that create delight. You catch when animations feel off by 50ms or color contrast doesn't meet WCAG standards. You believe speed and quality aren't opposites—they happen together when you're thoughtful about how you build.
- Drive outcomes with product thinking - You move from prototype to user feedback to iteration to production with speed and conviction. You own features end-to-end, challenge designs that compromise user experience, and care deeply about the entire user journey. You shape the product through action, not waiting for permission.
- Bring energy that elevates others - You see "impossible" UI challenges as puzzles waiting to be solved. Your optimism, intensity, and passion for craft are contagious and make the team stronger.
- Experience: 4+ years of shipping production-grade products
What You'll Do
- Architect scalable front-end systems - Design reusable component libraries, establish patterns for state management and API integration, optimize bundle sizes, and ensure the platform performs beautifully at scale. You'll work across the full spectrum from responsive layouts to micro-interactions, with bonus points for diving into Python backends when needed.
- Own and ship the primary user-facing features - Build the core interfaces users interact with daily. You'll write the React components, integrate with backend APIs, and polish every detail to production quality.
- Bridge AI capabilities with elegant user experiences - Partner with product and backend engineers to understand what's possible, then create interfaces that expose powerful functionality while hiding technical complexity.
- Multiply your impact across disciplines - You speak designer, engineer, and product fluently. Use AI tools to rapidly prototype design concepts in code and bridge gaps between teams with working demos instead of static mocks, turning ambiguous ideas into tangible experiences that drive better decisions.
About our Benefits
- Equity in addition to competitive cash compensation
- 100% Employer-Paid Medical, Dental & Vision options!
- Paid Parental Leave
- $500 Annual Learning Fund
- $150 Monthly Wellness Fund
- 401k via Betterment
- Relocation support to San Francisco Bay Area (where applicable)
- Office-related Perks:
+ BART or Caltrain to the office?: Contribute pre-tax funds to a Parking & Transit account, and you will never be taxed for it! + Lunchtime, Leveled Up: Enjoy curated local eats delivered to the office daily to enjoy with the team over an engaging conversation + Snack Central: Drinks and snacks for every craving - from healthy bites to Within team favorites. + Safe & Secure Bike Room: Commute in and safely store your bike.
Within is an equal opportunity employer. Within provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, national origin, sexual orientation, gender identity or expression, age, disability, genetic information, marital status or veteran status.
Compensation Range: $185K - $315K
Salary Context
This $185K-$315K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At within, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $185K to $315K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
within AI Hiring
within has 5 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Manhattan, NY, US. Compensation range: $205K - $315K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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