# Senior Machine Learning Engineer, Gen AI

**Company:** [Weave](https://hotfix.jobs/companies/weave)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, Go, LLMs, RAG, Prompt Engineering, Kubernetes, GCP, MLflow, Dagster, Triton Server, Postgres, Jupyter, Dvc, AWS
**Posted:** 2026-04-02

> 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.

## Job Description

## 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).

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**Apply:** https://hotfix.jobs/jobs/b9920278-7778-47e4-8d9b-89bd9d660640
**Canonical:** https://hotfix.jobs/jobs/b9920278-7778-47e4-8d9b-89bd9d660640