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.