Full-Time

Machine Learning Engineer

Enterprise

Boson Ai

Boson Ai

11-50 employees

Develops scalable AI tools for enterprises

No salary listed

Toronto, ON, Canada

In Person

Bachelor's, Master's

Category
AI & Machine Learning (1)
Required Skills
LLM
Rust
Python
Git
PyTorch
Machine Learning
RAG
TypeScript
Go

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Requirements
  • 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.
  • 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.
  • Proven ability to pay close attention to detail and prioritize quality, reliability, and security in technical work.
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.
Desired Qualifications
  • 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.

Boson AI develops large language model tools to power AI-driven experiences in virtual worlds. Its products understand and generate human-like text and are designed for wide use, from individuals to large enterprises. The tools work by combining advanced deep learning with system engineering to create customizable LLM-based applications that can be embedded into various software to personalize storytelling, learning, content creation, and data insights. Boson AI differentiates itself by focusing on tailored experiences in virtual environments across multiple industries, offering scalable solutions through product sales, subscriptions, and licensing. The company’s goal is to provide practical, personalized AI tools that enhance user interactions, storytelling, education, and business intelligence in virtual settings, becoming a leading provider in the AI market.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

Santa Clara, California

Founded

2023

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Simplify Jobs

Simplify's Take

What believers are saying

  • Boson AI raised $70 million, giving runway for model training and enterprise sales.
  • Fortune reported August 2026 enterprise targeting in finance, telecom, healthcare, and insurance.
  • Higgs TTS 3 and Higgs Avatar launched in June 2026, showing rapid product expansion.

What critics are saying

  • OpenAI, Meta, and Google can bundle voice into existing platforms and crush Boson distribution.
  • Higgs Realtime's beta timing and unfinished docs risk adoption delays before enterprise renewals in 2026.
  • If Boson cannot sustain model-quality leadership, its low-price pitch becomes a commodity trap.

What makes Boson Ai unique

  • Alex Smola launched Higgs Realtime on August 21, 2026, for live speech-to-speech agents.
  • Boson AI claims one-tenth competitor costs, with OpenAI-compatible realtime APIs and 100+ languages.
  • The company combines TTS, ASR, and avatar models into one voice-and-visual agent stack.

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Benefits

Flexible Work Hours

Growth & Insights and Company News

Headcount

6 month growth

8%

1 year growth

2%

2 year growth

22%
Boson AI
Aug 20th, 2026
Building voice AI that feels live: a hands-on guide to Higgs Realtime.

Building voice AI that feels live: a hands-on guide to Higgs Realtime. The Boson AI Team August 20, 2026 Real-time voice AI is easy to demo. Building it well is much harder. A good voice agent can't simply wait for someone to finish speaking, turn the audio into text, generate an answer, and read it back. Real conversations don't behave like a sequence of clean requests and responses. People pause. They interrupt. They change direction halfway through a sentence. They expect the system to remember what came before, use tools when necessary, and respond without making the conversation feel like a series of transactions. That changes how developers need to think about building voice applications - so today Boson is releasing the Higgs Realtime API Tutorial, a build-it-yourself guide for developers who want to understand how real-time voice AI works and build voice applications with sub-second latency at a fraction of the usual cost. Rather than starting with a finished demo and hiding the complexity underneath it, the tutorial builds a working browser voice assistant one layer at a time. From API calls to a live conversation. Traditional AI applications have a familiar rhythm: request, inference, response. Real-time voice runs on a different model of the world.

ScitiX
Jul 9th, 2026
Voice, built in: Boson AI's TTS and ASR models are now live on ScitiX Model Inference.

Voice, built in: Boson AI's TTS and ASR models are now live on ScitiX Model Inference. Venus Yang Speech is the most natural interface there is - and it's quickly becoming the default way people interact with AI. Today ScitiX is excited to announce that two of Boson AI's flagship speech models are available on the ScitiX Model Inference platform: bosonai/tts for text-to-speech, and bosonai/asr for speech recognition. Together they give you both halves of a voice experience - listening and speaking - behind a single API, on infrastructure that's built to be simple, fast, and secure. Meet the Boson AI models. Boson TTS is built for voice chat. It doesn't just read text aloud - it speaks, producing expressive, conversational speech that sounds like a person rather than a narrator. * Expressive & conversational - natural emotion, style, and prosody, with inline control * 100+ languages out of the box * Zero-shot voice cloning - capture a voice from a short sample, no fine-tuning required * $0.05 / minute If you're building voice assistants, agents, audiobooks, or any product where tone matters, bosonai/tts is designed to make the output feel alive. Boson ASR is a state-of-the-art speech recognition model that turns spoken audio into accurate text - reliably, across languages, and even when conditions aren't ideal. * State-of-the-art accuracy on real-world audio * 90+ languages supported * Robust in noisy environments - call centers, mobile, the real world * Streaming support for low-latency, real-time transcription * $0.006 / minute Pair the two and you have a full speech loop: bosonai/asr to understand what users say, bosonai/tts to respond in a natural voice. A platform designed to get you live in seconds. Great models are only useful if you can actually ship them. That's the whole point of ScitiX Model Inference - and it shows up in three places. Simple - discover, view, integrate. Every model on the platform lives in the Model Plaza, a searchable marketplace you can filter by provider, type, context window, and price. The flow is the same for every model: * Find the model - e.g. the bosonai/tts card in the Plaza. * View Details - read the spec, capabilities, pricing, and a ready-to-run code example. * Copy the endpoint and key into your config, set the model name, and you're done. js// config.js - model integration export default {baseURL: 'https://api.scitix.ai/model-api', apiKey: 'sk-scitix- - - - - - - - - - - - - ', model: 'bosonai/tts', // swap to 'bosonai/asr' for speech recognition} The API is OpenAI-compatible, so it slots into the tools and SDKs you already use. Fast - production-ready in seconds, not weeks. There's no provisioning, no model download, no infrastructure to stand up. From browsing the catalog to your first API call is a matter of seconds - any model in the Plaza is production-ready the moment you copy its endpoint and key. Behind the scenes, ScitiX handles the GPU capacity and scaling so your latency stays low as your traffic grows. And switching between models is trivial: the endpoint and authentication never change - the only thing that varies between bosonai/tts and bosonai/asr is the model name in your request. In its internal benchmarks, Boson TTS stays responsive across hardware tiers - first audio comes back in tens of milliseconds, with steady streaming throughput: Note: these figures are from internal test runs (Boson TTS streaming, single concurrent request, 100-sample suite) and are provided for reference only. They are test data, not a performance commitment or SLA, and may vary by workload, configuration, and region. Secure - your keys, your control. Authentication is a single Authorization: Bearer header carrying your API key. On the platform side, keys are always masked, can be rotated or revoked at any time, and account access can be protected with an extra layer of two-factor security plus security notifications. Your credentials stay yours. On the infrastructure side, your workloads run on ScitiX's own footprint: ScitiX operates Tier III+ (T3+) data centers in North America, and expects to complete its SOC 2 Type II certification by the end of 2026 - so the security story extends from your API key all the way down to the facilities your models run in. The takeaway. Boson AI's TTS and ASR give you a complete voice loop - expressive, natural speech and accurate, multilingual transcription - behind one OpenAI-compatible API. ScitiX Model Inference makes that loop simple to adopt, fast to scale, and secure by default, from your API key all the way down to the data center. Whether you're giving a product a natural-sounding voice or transcribing speech across 90+ languages, both models are live on ScitiX today - ready the moment you are.