Senior Software Engineer, LLM Platform

San Francisco, CA

About the 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 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 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 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 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 .

About Handshake

Handshake is a career network designed for students and recent graduates to find job opportunities. It has a community of over 10 million users from 1,400 educational institutions, including colleges and boot camps. The platform connects these individuals with more than 750,000 employers, covering various sectors. It aims to facilitate access to career resources for both students and employers.

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Job details

Location

San Francisco, CA

Company

NameHandshake
IndustryArtificial Intelligence (AI)
Team Size5

Funding

Total raised

$434M

Last stage

Growth

Investors

GGV Capital
Kleiner Perkins
Lightspeed Venture Partners
Spark Capital
True Ventures

Founders

GL

Garrett Lord

Co-Founder & CEO

LinkedIn
BC

Ben Christensen

Co-Founder

LinkedIn
SR

Scott Ringwelski

Co-Founder

LinkedIn

What happens next.

No applications, no recruiter spam. Just the intro.

01

Confirm the fit

A few questions to make sure this role is the right shape for you. Two minutes.

02

I pitch you to the company

I write the intro, send it to the founder, and handle the back-and-forth.

03

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.