Senior Machine Learning Engineer, Gen AI

Responsibilities

• Design and develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences.
• Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning.
• Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products.
• Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end.
• Build scalable, resilient services to support data integration, event processing, and platform extensions.
• Contribute to the continued evolution of product functionality that services large amounts of data and traffic.
• Write high-quality, performant, sustainable, and testable code.
• Coach and collaborate inside and outside the team.
• Work in a cloud environment with distributed components and services.
• Work with stakeholders to translate product goals into actionable engineering plans.

Requirements

• 5+ years of experience in structured back-end languages (Go, Java, Python; Go and Python a plus).
• Experience moving and storing TBs of data or 100M's to 10B's of records.
• Experience building and deploying ML driven B2B multi-tenant applications in production.
• Experience with ML technologies: Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow), DVC, Triton Server, LLMs, Postgres.
• Experience with modern ML tools: LLMs, RAG, Prompt Engineering, Fine Tuning, multi-modal models.
• Experience with data labelling or annotation for audio or text.
• Understanding of distributed systems and building scalable, redundant, observable services.
• Expertise in designing and architecting systems for distributed data sets and services.
• Experience with public clouds (AWS, GCP).
• Experience providing stable libraries and SDKs for internal use.

Nice-to-Haves

• Background in data analysis, visualization, presentation.
• 3+ years in data science, machine learning, or predictive analytics.
• Experience with natural language models, embeddings, inference at scale.
• Experience with real-time audio models (transcription, ASR, interruption detection, audio alignment, speech synthesis).
• Experience with Model Context Protocol (MCP).
• Proficient in containers, orchestrators (Kubernetes, GKE, Operator Pattern).
• Experience with sensitive data (PHI, HIPAA, PII).
• Experience with automation, container-based workflow engines, GitOps, IaC.
• Preference for open source solutions.
• Deep understanding of distributed data technologies (streaming, data mesh, data lakes, warehouses, distributed ML).