Member of Technical Staff, Small Language Models - Montreal - Onixai

    Onixai
    Onixai Montreal

    1 day ago

    Full time
    Description

    You will build and train the models that make Onix ours.

    Onix is building Personal Intelligence: AI that runs on your device, belongs to you, and protects your data by architecture. That means we cannot depend entirely on third-party APIs for inference. We need small, fast, specialized models that run where the user is. On their phone. On their laptop. At the edge. You will build them.

    This is not a research role where you optimize benchmarks on public datasets. This is applied ML engineering at a startup that ships daily. You will train small language models on proprietary expert knowledge. You will design multi-agent systems where specialized models work together. You will build the training infrastructure, define the data pipelines, and own the full lifecycle from experiment to production deployment.

    The constraint is what makes this interesting. You are not training a frontier model with unlimited compute. You are making something small enough to run on a phone, smart enough to be useful, and specialized enough to represent a specific expert's thinking. That is a harder problem than throwing more GPUs at a bigger model.

    What You Will Do

    • Train and fine-tune small language models on proprietary expert content for on-device deployment
    • Design and build multi-agent orchestration systems where specialized models collaborate
    • Own the full ML pipeline: data preparation, training infrastructure, evaluation, optimization, and deployment
    • Optimize models for on-device inference: quantization, distillation, pruning, and architecture search
    • Work with the product team to define what intelligence needs to live on-device versus in the cloud

    Who You Are

    You have trained models from scratch, not just fine-tuned open-source checkpoints with a LoRA adapter. You understand what happens inside a transformer at the level where you can make architectural decisions, not just hyperparameter choices. You have shipped models to production and dealt with the gap between benchmark performance and real-world usefulness.

    You are excited by constraints. Unlimited compute is boring to you. Making a 1B parameter model outperform a 70B model on a narrow task is the kind of problem that gets you out of bed. You have opinions about tokenizer design, data curation, and why most fine-tuning pipelines produce mediocre results.

    You have:

    • Deep experience training language models: pretraining, fine-tuning, RLHF, DPO, or similar alignment techniques
    • Strong PyTorch skills and experience with training infrastructure (distributed training, mixed precision, efficient data loading)
    • Hands-on experience with model optimization for deployment: quantization, distillation, ONNX, CoreML, or similar
    • Understanding of multi-agent system design and orchestration patterns
    • Experience with the full ML lifecycle, not just experimentation

    You are:

    • A builder, not a researcher. You care about shipped models, not paper citations
    • Obsessed with efficiency. You find elegance in making small models punch above their weight
    • Comfortable working across the stack, from GPU kernels to API endpoints
    • Opinionated about data quality. You know that training data matters more than architecture for most tasks

    This Role is NOT For You If:

    • Your ML experience is primarily using APIs from OpenAI, Anthropic, or similar providers
    • You have never trained a model from scratch
    • You need a large ML infrastructure team to be productive
    • You are looking for a pure research position with no production pressure
    • You want a remote job

    What Success Looks Like

    • A proprietary small language model is running on-device and outperforming generic API calls for our specific use cases
    • The multi-agent orchestration system handles complex user queries by routing to the right specialized models
    • Training pipelines are reproducible, documented, and can incorporate new expert content without starting over
    • Model inference latency on mobile meets the bar for a responsive user experience
    • You have a clear roadmap for what to train next, informed by product needs and user data

    How to Apply

    Submit your application and answer:

    • Describe a model you trained and deployed to production. What was the task, what was the architecture, and what was the hardest engineering problem you solved?
    • How would you approach training a small language model to faithfully represent a specific expert's knowledge and reasoning style?
    • What is your take on when to train your own model versus fine-tuning an existing one versus just prompting a frontier model? Where are the breakpoints?

    Onix announces Guillaume Mercier as Chief Operating Officer, bringing decades of executive leadership experience from BDC, Vention, SSENSE, and Cirque du Soleil.

    Every cycle in tech swings between centralization and decentralization. Cathedrals rise, but the bazaar always returns. Watch David's keynote on why Personal Intelligence is the future.

    Onix has been selected as a Top 100 AI Startup and will be featured at ALL IN Montreal.


    #J-18808-Ljbffr
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