Senior Software Engineer, Machine Learning Infrastructure

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.

• 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 product ionize new models and establish best practices for ML infrastructure across Handshake.
• Improve the reliability, scalability, and developer experience of our ML platform.

• 5+ years of production software engineering experience using Python, Go, Type Script, 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 Big Query, 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.

• Experience with Ray, Anyscale, Kube Ray, 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.

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

$176K – $220K

For cash compensation, we set standard ranges for all U.S.

-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.