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About This Role
About Handshake
Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million\+ employers, and 1,600 educational institutions.
In 2025, we started Handshake AI and built the fastest\-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to \~$1B run rate and pay \~$60M to over 30K individuals every month.
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
- Partner hand\-in\-hand with world\-class AI labs, Fortune 500 partners and the world’s top educational institutions
- Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders
- Build a massive, fast\-growing business with billions in revenue
About Handshake AI
Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data\-intensive post\-training techniques. We believe that data spend for AI training will increase by 3\-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.
About the Role
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We’re looking for a Senior Software Engineer to join our ML Infrastructure \& Platform team. This team powers both Handshake’s core career marketplace and Handshake AI by building the shared infrastructure behind our production ML and AI systems.
This is an infrastructure\-heavy role for an engineer who enjoys building scalable platforms at the intersection of software engineering, machine learning, and generative AI. You’ll help teams move quickly from prototype to production while building the reliable, high\-performance systems that power training, evaluation, and inference across Handshake.
What You’ll Do
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- Build and operate the shared infrastructure behind production ML and AI, including data pipelines, feature stores, training, and model serving.
- Develop and scale our LLM platform, including provider integrations, orchestration, observability, and controls for cost, latency, and reliability.
- Build evaluation infrastructure, including LLM eval harnesses, benchmarks, and quality measurement pipelines.
- Support post\-training workflows, including fine\-tuning, reinforcement learning pipelines, and supporting data infrastructure.
- Optimize inference infrastructure for open and fine\-tuned models, including GPU serving, batching, and autoscaling.
- Partner with AI, Data Science, and Product teams to productionize new models and establish best practices for ML infrastructure across Handshake.
- Improve the reliability, scalability, and developer experience of our ML platform.
Desired Capabilities
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- 5\+ years of production software engineering experience using Python, Go, TypeScript, or similar languages.
- Experience building and operating cloud infrastructure on AWS, GCP, or similar platforms.
- Strong experience with Kubernetes, Docker, Terraform, CI/CD, and operating production services.
- Hands\-on experience building ML infrastructure, including model serving, training pipelines, feature stores, embeddings, or ML observability.
- Experience with modern data platforms such as BigQuery, Airflow, Spark, Beam/Dataflow, or streaming pipelines.
- Practical experience building production systems with LLMs or generative AI, including orchestration, provider APIs, observability, and performance optimization.
- Strong systems design skills, sound engineering judgment, and the ability to thrive in ambiguous, fast\-moving environments.
Extra Credit
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- Experience with Ray, Anyscale, KubeRay, Ray Serve, vLLM, Triton, PyTorch, or GPU\-backed inference and training.
- Experience designing LLM evaluation frameworks, benchmarking systems, or quality regression testing.
- Experience with Vertex AI, Bigtable, Redis, or feature platform infrastructure.
- Experience with post\-training techniques such as fine\-tuning, RLHF, reinforcement learning, or reward modeling.
- Experience building agentic systems, MCP integrations, tool use, memory systems, or voice AI applications.
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: $176K \- $220K
Salary Context
This $176K-$220K range is above the median 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 ($198K) sits 9% below the category median. Disclosed range: $176K to $220K.
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 San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 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
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