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About This Role
Location
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San Francisco, CA
Employment Type
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Full time
Location Type
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On\-site
Department
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Engineering
Compensation
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- $176K – $220K
*For cash compensation, we set standard ranges for all U.S.\-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.*
About Handshake
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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 \& peers can see \& feel
- Work hand\-in\-hand with world\-class AI labs, Fortune 500 partners \& the world's top educational institutions
- Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, \& Palantir, among others
- Build a massive, fast\-growing business with billions in revenue
The Role
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Handshake is building the infrastructure layer that powers the next generation of AI agents across our platform. As a Senior Software Engineer on our Agentic Infrastructure team, you'll be at forefront of AI at Handshake, will architect \& build the foundational systems that allow AI agents to plan, execute, \& operate autonomously at scale. This is a rare greenfield opportunity to define how an AI\-first company builds for the agentic era — at the intersection of infrastructure, AI, \& direct product impact.
- Architect \& build the core agent orchestration layer — including tool use, memory, \& multi\-step reasoning systems — that powers AI\-driven features for 20M\+ users \& 1M\+ employers across Handshake's platform
- Design evaluation, observability, \& reliability frameworks that ensure agent behavior is safe, auditable, \& production\-ready at scale
- Establish engineering standards for agentic development across Handshake's platform teams, enabling every engineer to build with AI
- Partner with ML, product, \& platform engineers to ship agent\-powered features from infrastructure to production at Olympic pace
You Have
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Must\-Haves:
- 4\-7 years of backend engineering experience, with strong proficiency in NodeJS, Typescript, \&/or Python
- Experience designing \& operating distributed systems at significant scale for developer experience
- Hands\-on experience building with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)
- Deep understanding of agent architectures: tool use, multi\-step reasoning, memory management, \& evaluation
- Experience with cloud infrastructure (AWS, GCP, or Azure) \& infrastructure\-as\-code tools like Terraform
- Experience with Node, JavaScript, CICD, Docker
- A track record of shipping production systems with high reliability, low latency, \& strong observability
- Strong communication skills \& ability to drive alignment across engineering \& cross\-functional partners
Bonus Points:
- Open\-source contributions to LLM or agent frameworks
- Prior experience at an AI\-native company or on a dedicated AI platform team
We Offer
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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, \& vision, mental health support, $500 wellness stipend
Growth: $2,000 learning stipend, ongoing development
Remote \& Office: Internet, commuting, \& 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, \& comprehensive US benefits at joinhandshake.com/careers.
Salary Context
This $176K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 9% above the category median. Disclosed range: $176K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Handshake AI Hiring
Handshake has 11 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager. Based in San Francisco, CA, US. Compensation range: $175K - $325K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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