AI Engineer

Vision

Stochastic is an AI company focused on building a fast, private, and trustworthy conversational AI agent system for serious enterprises. Stochastic is building the next generation of AI agents that can autonomously plan and perform actions to solve problems while conducting natural, real-time conversations - something previously thought impossible due to fundamental latency-intelligence tradeoffs. Our full-stack AI agent platform, purposely built and vertically optimized, transforms industry-wide challenges into strategic advantages through specialized models that achieve both speed and intelligence, with a particular focus on serving highly regulated industries where both performance and compliance requirements must be met simultaneously. We are actively working with leading financial services and healthcare organizations to transform customer service operations with sophisticated, compliant AI conversations and automation.

Team

Stochastic is founded by a team of Harvard University AI systems researchers that built the world's first Bayesian and LLM inference accelerators and a real-time speech and natural language processing engine. The founding team is joined by experts from Stanford, CMU, UIUC, NVIDIA, Meta and other top AI research organizations with the passion for making AI more accessible to everyone. Our recent research includes latency-optimized transformers architecture, quantized parameter efficient fine-tuning, and sparsity-aware throughput maximization on GPUs.

Role Overview

We are looking for a Machine Learning Engineer to take ownership of building and optimizing agent pipelines, along with developing ML models, ML pipelines, and accelerating deep learning models. The primary focus of this role is designing and implementing efficient agent pipelines. You should have a strong interest in solving complex challenges related to ML acceleration, be research-oriented, and possess deep expertise in best practices within your field. A highly self-motivated and proactive approach is essential for success in this role.

Responsibilities

As a Machine Learning Engineer, you will:

• Lead the design and development of agent pipelines, ensuring efficiency, scalability, and reliability.
• Build and optimize ML models and ML pipelines, with a strong emphasis on accelerating deep learning models.
• Fine-tune, accelerate, and deploy LLMs within our existing pipelines.
• Conduct research and experiments on the latest techniques for fine-tuning, acceleration, and agent pipeline optimization.
• Oversee the specification, development, testing, and release of new features.
• Provide expert support for strategic customers on deployment and scalability challenges.
• Contribute to the strategic planning of xChat and xCloud, Stochastic’s core products.

Requirements

You are a great fit for this role if you have:

• A degree in Computer Science.
• Hands-on experience with Retrieval-Augmented Generation (RAG) systems and agent-based architectures.
• Strong proficiency in Python and experience with databases like MongoDB.
• Knowledge of RESTful APIs, microservices architecture.
• Experience with version control systems (Git) and agile software development methodologies.
• Experience fine-tuning deep learning models using PyTorch and the Transformers library.
• Expertise in deploying deep learning models in production environments, optimizing for efficiency and scalability.
• Familiarity with building and managing ML and agent pipelines, ensuring seamless model integration and orchestration.
• Experience with at least one major public cloud provider (AWS, Azure, or GCP).
• Proficiency in Kubernetes for scalable deployment and orchestration of ML workloads.

Strong pluses

• Experience working on accelerating models
• Expertise in distributed systems and large-scale ML infrastructure.
• Experience with Terraform for infrastructure as code.
• Past experience as an ML Engineer at a SaaS company, contributing to production-grade ML solutions.
• Experience overseeing and mentoring a team of engineers.
• Familiarity with Go for building high-performance ML services and infrastructure.