Senior Software Engineer, Machine Learning Infrastructure

Handshake•San Francisco, CA

6d•$176,000 - $220,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.