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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, Machine Learning Infrastructure - **Company:** Handshake - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $176,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, BigTable, BigQuery, Cloud Computing, Continuous Integration, Data Infrastructure, Data Flow Control, Python (Programming Language), Machine Learning, Performance Tuning, Redis, Regression Testing, Azure Machine Learning, Software Engineering, Systems Integration, TypeScript, Reinforcement Learning, Cloud Platform System, Pytorch, Large Language Models, Multi-Agent Systems, Apache Spark, Generative AI, Kubernetes, Machine Learning Operations, Terraform, Data Pipelines, Docker, Golang - **Published:** July 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8c5f1eb5fe027fb0 ## About the Role * 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. ## Description 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. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)