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Replicant

Automated conversational AI for customer service

Senior Machine Learning Engineer

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of software development experience in machine-learning infrastructure, machine-learning tooling, or products using machine learning.
  • Experience with the machine-learning ecosystem, such as Python, PyTorch, FAISS, or Elasticsearch, is preferred.
  • Ability to tackle ambiguous technical problems and independently solve complex, high-impact problems.
  • Interest in exploring the nuance and aesthetic of conversations.

Responsibilities

  • Lead exploration and application of large language models and generative artificial intelligence in new areas.
  • Translate recent research into high-performing systems and models that can be practically applied to enhance user experiences.
  • Help set the team's strategic direction and cultivate an environment that encourages innovation and professional growth.
  • Engage in all aspects of development, from ideation and experimentation through implementation and deployment.
  • Collaborate with teams and product managers to develop and implement machine-learning solutions, optimize performance, and align solutions with broader business goals.

Desired Qualifications

  • Equity ownership in the company.

About the Company

Replicant

Automated conversational AI for customer service

Replicant provides cloud-based conversational AI for customer service. It builds automated chat and voice agents that handle customer inquiries, answer questions, and resolve problems without human intervention, as a service (subscription). How it works: clients integrate Replicant’s AI into their contact channels (phone, chat, etc.), and the system uses natural language understanding to interpret queries, generates responses, and can hand off to human agents if needed; data is stored and analyzed to improve performance, with a focus on anonymous data usage for privacy. How it differs: emphasizes privacy and data security, offering anonymous data handling, and a subscription model with options to upsell or add features; serves a wide range of industries from retail to banking, aiming to automate end-to-end customer support. Goal: help businesses cut costs and boost efficiency by automating customer service interactions while protecting user privacy.

Company size
51-200
Company stage
Series B
Total funding
$120M
Headquarters
San Francisco, CA
Founded
2017

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