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ML - GenAI Engineer, Voice & Speech

Weave· Technology
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RemoteFull-timePosted 7mo ago

About the role

Machine Learning Engineer focused on GenAI, voice and speech with remote opportunity in India. Join autonomous cross-functional teams to design, build, and deploy ML infrastructure, models, and platforms enabling AI-powered features at Weave.

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 code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce.
  • Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices.
  • Work in a cloud environment, considering the implementation of functionality through several distributed components and services.
  • Work with our stakeholders to translate product goals into actionable engineering plans.

Qualifications

  • 3-5 years of experience in Machine Learning or AI, preferably with a focus on natural language.
  • 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 environments at scale for external products and customers.
  • Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow, etc), DVC, Triton Server, LLMs, Postgres, and others.
  • Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, LLM evaluations, multi-modal models, and others.
  • Experience with data labelling or annotation for audio or text use cases.
  • Understanding of distributed systems and building scalable, redundant, and observable services.
  • Expertise in designing systems for distributed data sets and services.
  • Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.).
  • Experience providing stable well designed libraries and SDKs for internal use.
  • Self driven and a thirst for learning in a quickly changing industry.
  • Demonstrated track record of delivering complex projects on time and have experience working in enterprise-grade production environments.
  • Demonstrated capacity for leadership or mentorship.
  • Strategic thinker with a strong technical aptitude and a passion for execution.

Skills

Required:Low latency NLP pipelines
Nice to have:Data analysis and visualizationReal-time audio models and voice use casesModel Context Protocol (MCP)

Benefits

  • Fully remote opportunity in India with overlap to India and US business hours
Last updated Oct 8, 2026. Interested? Apply on the company site.
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About Weave

Customer communication and payments platform.

As the pace of innovation continues, it is growing more difficult to see the value of technology. Often meaningful information is inaccessible at the time it would be most beneficial. Weave threads together data, software and communication platforms to build stronger relationships at the point of contact by making valuable information instantly available in simple and intuitive formats. This delivers improved productivity, better collaboration and greater insights making it easier to meet the needs of patients and to build better business and clinical outcomes.

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