Senior Machine Learning Engineer, Gen AI
Builds ML infrastructure, models, and platforms to enable AI-powered features for developers handling massive datasets. Requires 5+ years backend experience, production ML deployment, and expertise in LLMs, RAG, distributed systems, and cloud platforms.
About the job
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).
Skills
Python, Go, LLMs, RAG, Prompt Engineering, Kubernetes, GCP, MLflow, Dagster, Triton Server, Postgres, Jupyter, Dvc, AWS
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