Data Platform Engineer (AI/ML)

TL;DR

Data Platform Engineer (AI/ML): Building foundational systems for hirify.global’s AI-driven products and data experiences with an accent on ML platform, data pipelines, and self-serve analytics. Focus on designing scalable infrastructure for model serving, feature stores, workflow orchestration, and production monitoring to empower Data Science and integrate AI into products.

Location: San Francisco, CA; New York, NY hubs or remotely in the United States only.

Annual Base Salary Range: $235,000 - $376,000 USD (SF/NY hubs; remote localized 80-100% of range).

Company

hirify.global’s platform empowers teams to design, prototype, and collaborate in real-time with AI integration from idea to product.

What you will do

• Lead hirify.global’s AI data agent: own data-agent layer, build prompt-processing pipelines, instrument interactions, and deliver usage analytics.
• Own and evolve ML and data platform: model serving, feature pipelines, workflow orchestration, CI/CD for models, production monitoring.
• Build product-facing data systems to integrate models and data into hirify.global’s product experience.
• Ship tooling for Data Science: feature stores, rollout systems, observability.
• Design infrastructure for AI-assisted natural language interfaces to data for self-serve analytics.
• Drive cross-functional initiatives aligning data contracts, SLAs, and designs with Data Science, AI/ML, Infrastructure, and Product.
• Improve developer experience for ML and data practitioners via abstractions and platform capabilities.

Requirements

• 5+ years in data platform, infrastructure, or ML engineering; 1+ years on AI/ML systems.
• Experience building/operating end-to-end ML systems in production (training, evaluation, deployment, monitoring).
• Strong Python (or similar) for reliable, scalable systems/services.
• ML infrastructure design: model serving, feature pipelines, workflow orchestration, scalable architectures.
• Cross-functional work driving projects across Data Science, Engineering, Infrastructure, Product; data modeling, data product design.
• Must be based in the US (hubs or remote).

Nice to have

• ML platform tooling: MLflow, Kubeflow, feature stores, CI/CD for ML.
• LLMs, RAG, prompt processing, AI-native infrastructure.
• Self-serve analytics platforms or internal data tools.
• Modern data stack: Snowflake, dbt, Dagster; cloud like AWS.
• Product mindset connecting platform to user/business impact.

Culture & Benefits

• Equity, health/dental/vision, retirement with company contribution, parental leave, mental health support.
• Generous PTO, company recharge days, learning & development stipend, work from home stipend, cell phone reimbursement.
• Annual bonus for eligible roles; sales incentives where applicable.
• Grow as you go: hiring curious people excited to learn; encourage applying even if not perfect fit.
• Equal opportunity employer; accommodations for disabilities; cameras on in video interviews; in-person onboarding if hired.