---
title: 'Machine Learning Engineer - Enterprise at Boson AI'
canonical: 'https://feeny.ai/job/machine-learning-engineer-enterprise-boson-ai-toronto-cm87w800xvcq'
type: 'job'
last_seen: '2026-09-11'
---

# Machine Learning Engineer - Enterprise at Boson AI

- **Company:** Boson AI
- **Location:** Toronto, Canada
- **Compensation:** $150k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-06-18
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.lever.co/bosonai/ab98b4e4-df64-4c94-b66e-f86e7bac63ae

## Job description

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards.

About the Role: We are seeking a skilled, detail-oriented, and passionate Machine Learning Engineer to join our enterprise team. In this pivotal role, you will be at the forefront of developing and deploying groundbreaking AI solutions. This involves integrating advanced language/voice/vision models, mastering fine-tuning techniques, building sophisticated workflows and platforms, and pioneering innovative agentic approaches. You will immerse yourself in challenging problems that demand a deep understanding of model behavior, meticulous implementation, and an unwavering commitment to quality and reliability in enterprise environments. A key and exciting aspect of this role is contributing to the architecture and implementation of intelligent systems where AI agents can perform complex tasks autonomously, interacting with diverse data sources and tools, as we collectively move towards building truly cohesive and powerful AI capabilities for our clients.

## Responsibilities

- Deliver solutions end to end that meet the needs of our customers - understanding user pain points, scoping product specs, and designing and building LLM-powered software.
- Benchmark the model, and help write evals for customers to identify model weaknesses.
- Develop and deploy modern search systems (e.g., RAG, DeepSearch) to enhance model performance, grounding, and the ability to utilize enterprise-specific knowledge.
- Implement and optimize techniques for fine-tuning and align large models on domain-specific data.
- Ensure the quality, reliability, security, and scalability of models and agentic systems through meticulous attention to detail, diligent execution, and continuous monitoring in demanding enterprise settings.
- Integrate individual AI components into a scalable platform.

## Qualifications

- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
- Strong contribution record on GitHub. Please include your GitHub link in your application.
- Experience working with large language or multimodal models and their applications.
- Experience implementing and working with search systems.
- Proven ability to pay close attention to detail and prioritize quality, reliability, and security in technical work.
- Proficiency in programming languages (e.g., Python, Rust, TypeScript or Go) and relevant ML frameworks (e.g., PyTorch, JAX).
- Demonstrated ability to design, chain, or orchestrate multiple models (especially LLMs) to create multi-step pipelines or workflows for task automation.

Bonus Points

- Experience developing or contributing to agentic AI products or systems.
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices.
- Familiarity with distributed training and inference techniques.
- Experience with system design, API development, and building scalable infrastructure for deploying and managing AI models or agentic systems.
- Understanding of enterprise software integration patterns and data security considerations.
- Solid understanding of HTTP protocol and real-time communication protocols (e.g., WebRTC) for voice AI.
- Excellent problem solving skills.
- Ability to work independently and drive projects forward in a fast-paced environment

## About Boson AI

## Company Overview
- **One-liner**: Boson AI builds real-time, audio-native AI models and agentic systems for natural, conversational voice interactions with machines.
- **Entity Type**: Private (funding not disclosed)
- **Headquarters**: Santa Clara, California, United States (also Toronto, Canada office)
- **Founded**: 2023
- **Founders**: Dr. Alex Smola (Co-Founder & CEO/CSO), Dr. Mu Li (Co-Founder & CEO/CTO), Yi Zhu (Co-Founder, departed May 2026), Yizhi Liu (Co-Founder)

## Core Business
- **Primary industries**: Conversational AI, Voice AI, Multimodal AI, Enterprise AI infrastructure
- **Target customers**: B2B – enterprises deploying voice agents, real-time customer service, and AI-powered communication workflows
- **Mission**: “Make communication with machines as easy, natural and fun as talking to a human.”

## Products & Services
- **Higgs Realtime**: An end-to-end, audio-native real-time speech-to-speech model for enterprise voice agents. Supports interruptions, code-switching (100+ languages), ~700ms speech-in to speech-out latency, and API compatibility with OpenAI Realtime (change three lines of code). Pricing: $0.0023/min audio in, $0.014/min audio out. [LinkedIn post](https://linkedin.com/company/boson-ai)
- **Higgs Avatar v1**: Real-time conversational avatar foundation model that generates 480p video at 16 FPS from a single image and streaming audio. Designed for real-time digital presence in voice agents. Private preview announced May 2026. [LinkedIn post](https://linkedin.com/company/boson-ai)
- **Agent Platform (Agent OS)**: An internal platform for building, deploying, and orchestrating AI agents (referenced in job postings as “Member of Technical Staff - Agent Platform”).

## Market Standing
- **Valuation**: Not publicly available
- **Key Metric**: Total funding not disclosed; headcount of 32 employees (as of mid-2026)
- **Notable Investors/Partners**: Not publicly listed. Talent sources include Amazon Web Services (AWS), Google, University of Toronto, Vector Institute, indicating strong ties to top AI research and engineering communities.
- **Growth Signals**: Launch of Higgs Realtime (August 2026) and Higgs Avatar v1 (May 2026); active hiring for senior engineering and ML roles; 10,000+ LinkedIn followers with +2.5% monthly growth; technical team comprises 68% of workforce.

## Competitive Advantages
- **Full-stack AI ownership**: Builds own foundation models (audio, avatar, agent orchestration) rather than stitching external components, enabling deep co-design for low latency and natural interaction.
- **Real-time expertise**: Achieves ~700ms speech-in to speech-out and ~125ms barge-in yield, with top benchmarks in tool-calling and interruption recovery.
- **Cost efficiency**: Higgs Realtime pricing ($0.0023/min audio in) is significantly lower than many competitors, targeting enterprise-scale deployment.
- **Research pedigree**: Founders have “almost a century of expertise in AI” (Alex Smola and Mu Li are well-known ML researchers; Smola co-authored seminal work on kernel methods and scalable ML).

## Strategic Focus
- **Real-time conversational AI for enterprise**: Prioritizing production-ready voice agents that can handle interruptions, code-switching, and emotional alignment.
- **Multimodal expansion**: Adding visual presence (avatars) to voice agents to make interactions more natural and human-compatible.
- **Enterprise deployment**: Offering self-hosting options and private previews for large-scale customers.
- **Building the full agentic pipeline**: From data and modeling to training, tuning, and serving – all in-house.

## Why Work Here
- **Culture**: Described as “a diverse group of researchers, engineers, and industry specialists united by a passion for innovation.” Emphasis on building scalable AI that serves millions.
- **Work policy**: Most roles are on-site at Santa Clara HQ or Toronto office. A Site Reliability Engineer role was listed as remote (Toronto). Likely hybrid/on-site for core engineering.
- **Engineering environment**: Deep technical stack (PyTorch, TensorFlow, Kubernetes, NVIDIA, Supermicro, etc.). Opportunity to work on frontier AI models and real-time systems.
- **Growth stage**: Small team (~32 people) with strong research roots; employees have previously worked at AWS, Google, Robinhood, and alumni go to OpenAI, Anthropic, xAI – indicating high-caliber talent and career mobility.
- **Hiring process**: Uses AI tools to assist with resume review and analysis, but final decisions made by humans.

## Sources
1. [boson.ai/about](https://www.boson.ai/about)
2. [boson.ai/about/team](https://www.boson.ai/about/team)
3. [linkedin.com/company/boson-ai](https://linkedin.com/company/boson-ai)
4. [jobs.lever.co/bosonai](https://jobs.lever.co/bosonai)

## Other roles at Boson AI

- [Datacenter Technician](https://feeny.ai/job/datacenter-technician-boson-ai-barrie-11tetdz9z3vr) — Barrie, Canada
- [Software Engineer - Platform & Application](https://feeny.ai/job/software-engineer-platform-application-boson-ai-santa-clara-zct1esjbfzcn) — Santa Clara, CA
- [Site Reliability Engineer](https://feeny.ai/job/site-reliability-engineer-boson-ai-toronto-69xyfzggnjaj) — Toronto, Canada
- [Senior Software Engineer - Systems](https://feeny.ai/job/senior-software-engineer-systems-boson-ai-santa-clara-94h0qsa26h84) — Santa Clara, CA
- [Frontend Engineer](https://feeny.ai/job/frontend-engineer-boson-ai-santa-clara-nbsv99agxh8b) — Santa Clara, CA
- [Member of Technical Staff - Agent Platform (Agent OS)](https://feeny.ai/job/member-of-technical-staff-agent-platform-agent-os-boson-ai-santa-clara-95vzvadf5n6c) — Santa Clara, CA