AI/ML Engineer

Direct Hire positions: They want local candidates to bay area. Preferred colleges are MIT, California Berkley, Santa Clara, Stanford, California State, Harvey Mudd, SF University. Jr to Mid level experience but looking for top of the class candidates.

Company Overview:

We're building AI-powered Digital Twins for businesses. A Digital Twin is an AI version of an employee - trained on their decisions, knowledge, and communications - that colleagues can query anytime. Think of it as being able to ask a coworker for help even when they're busy, on vacation, or have left the company.


Most AI assistants are built for individuals; Viven's are designed to be shared. Organizations can create Twins for key leaders, subject matter experts, critical workflows, or entire business units - preserving institutional knowledge and making it accessible in real time. Enterprise-grade privacy and fine-grained access controls ensure Twins only reveal information users are entitled to see. The result: teams move faster, critical expertise doesn't walk out the door, and an organization's most in-demand people can scale their impact without being in every meeting.

Role Overview:


We are looking for an AI/ML Engineer with hands-on experience in machine learning, natural language processing, and large language models. This AI/ML Engineer will play a critical role in designing, training, and deploying ML models that power Viven's products, with a focus on performance, scalability, and reliability.

This opportunity is an ideal role for those seeking to build and deploy machine learning models at scale, and thrives in an environment where research translates rapidly into real-world systems.

Key Responsibilities:

• Design, train, and deploy machine learning models for production use at scale.
• Work on LLM-based systems - from fine tuning to building agents and evaluation pipelines.
• Collaborate with engineers and product teams to scope problems, define success metrics, and build robust evaluation frameworks.
• Mitigate LLM challenges: address biases, hallucinations, and knowledge cutoff limitations to improve trust and performance.
• Optimize models for latency, accuracy, and scalability in real-world applications.
• Contribute to end-to-end product development - from research to deployment to iteration.

Qualifications:

• 1+ years of experience in Machine Learning and/or Data Science, with a track record of shipping ML systems into production.
• Strong understanding of Machine Learning and Deep Learning libraries, such as Scikit Learn, Keras, TensorFlow, PyTorch, Theano, or DyLib
• Deep expertise in at least one of: the following:
• Search / retrieval / ranking or recommendation systems
• Generative AI product development (shipping beyond demos, building evals, problem scoping)
• LLM agent products
• LLM fine tuning
• Practical understanding of LLM limitations (bias, hallucinations, knowledge cutoffs).
• Experience working at an early-stage startup or on 0→1 product launches.
• Comfort with both research prototyping and production-grade engineering.