Interested in this AI/ML Engineer role at Handshake?
Apply Now →Skills & Technologies
About This Role
About Handshake
===================
Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50\), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full\-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
Why join Handshake now:
- Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel
- Work hand\-in\-hand with world\-class AI labs, Fortune 500 partners and the world’s top educational institutions
- Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others
- Build a massive, fast\-growing business with billions in revenue
About the Role
------------------
Handshake AI Enterprise is a new business unit building AI\-powered applications for the world's largest companies. We deploy small, expert teams directly into enterprise environments — deeply learning each customer's business, defining where AI can drive real impact, and building and iterating on agents that automate their most critical workflows. We're not a consulting firm and we're not selling software. We're building and deploying AI that measurably changes how enterprises operate.
We're looking for a Senior Frontend Engineer who wants to architect the foundation — not just build on top of it.
As our first senior frontend hire, you'll define how we build UI across the entire team. That means establishing the component system, design language, and engineering patterns that every engineer who comes after you will rely on. You'll set the technical bar, make the foundational decisions, and build in a way that scales — so others can move fast without sacrificing quality.
This is a rare opportunity to own the frontend architecture of a new product from zero, at a company with the data, lab relationships, and momentum to win.
Location: San Francisco, CA \| 5 days/week in\-office
- Architect the frontend foundation for Handshake AI Enterprise — component system, design tokens, patterns, and engineering conventions that the whole team builds on
- Make foundational technology decisions: framework choices, state management, rendering strategy, performance budgets
- Build shared libraries and abstractions that let other engineers move fast without reinventing the wheel or sacrificing quality
- Establish code standards, review practices, and documentation that scale as the team grows
- Collaborate closely with Applied AI and Platform engineers to define clean API contracts and integration patterns
- Ship production\-grade UI for enterprise customer deployments alongside building the foundation
- Evolve the architecture over time as the team, product, and customer base scales
Desired Capabilities
------------------------
- 5–6\+ years of frontend engineering experience with a strong architectural track record
- Deep expertise in ReactJS and TypeScript — you understand the tradeoffs, not just the syntax
- Proven experience building and maintaining shared component libraries or design systems used by other engineers
- Strong opinions on frontend architecture — state management, rendering patterns, performance, accessibility — and the ability to defend and evolve them
- Experience making technology decisions that others depend on, with an eye toward long\-term maintainability
- Comfort working in ambiguous, fast\-moving environments where the right answer isn't always obvious
- Strong communication skills — you'll need to align engineers, product, and design on how things get built
Extra Credit
----------------
- Experience building frontend architecture for AI\-powered or data\-intensive applications
- Background at a company known for high craft — you've seen what a great design system looks like from the inside
- History of writing technical documentation or guides that helped other engineers ramp quickly
- Experience with performance optimization, accessibility standards, or cross\-platform rendering at scale
- Familiarity with enterprise deployment environments or security constraints
Perks
=========
Handshake delivers benefits that help you feel supported—and thrive at work and in life.
*The below benefits are for full\-time US employees.*
Ownership: Equity in a fast\-growing company
Financial Wellness: 401(k) match, competitive compensation, financial coaching
Family Support: Paid parental leave, fertility benefits, parental coaching
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
Growth: $2,000 learning stipend, ongoing development
Remote \& Office: Internet, commuting, and free lunch/gym in our SF office
Time Off: Flexible PTO, 15 holidays \+ 2 flex days
Connection: Team outings \& referral bonuses
Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.
Compensation Range: $205K \- $300K
Salary Context
This $205K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Handshake, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($252K) sits 15% above the category median. Disclosed range: $205K to $300K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Handshake AI Hiring
Handshake has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $170K - $416K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.