at Convergence
Full TimeMachine Learning Engineer
At Convergence, we're transforming the way AI integrates into our daily lives. Our team is developing the next generation of AI agents that don't just process information but take actions, learn from experience, and collaborate with humans. By introducing Large Meta Learning Models (LMLMs) that integrate memory as a core component, we're enabling AI to improve continuously through user feedback and acquire new skills during real-time use.
We believe in freeing individuals and businesses from mundane, repetitive tasks, allowing them to focus on innovative and creative work that truly matters. Our personalised AI assistants collaborate with users to enhance productivity and creativity. With a recent $12 million pre-seed funding from Balderton Capital, Salesforce Ventures, and Shopify Ventures, we're poised to make a significant impact in the AI space. Join us in shaping the future of human-AI collaboration and be part of our mission to transform the AI landscape.
We are looking for talented ML engineers and researchers to join our team and focus on training models which power Proxy, our generalist agent.
You will work with a small team — equipped with lots of GPUs – to train models, including multi-modal vision LLMs and action models.
You will also be laying the foundations of machine learning engineering at Convergence, utilising tools and best practices to improve our ML workflows.
Your role will span the full stack of model training, including:
Implementing and testing different fine-tuning and preference learning techniques like DPO
Building datasets through scrappy methods, including synthetic data pipelines, data scrapers, combining open source datasets, and spinning up data annotations
Conducting experiments to find good data mixes, regularisers, and hyperparameters
At Convergence, members of technical staff own experiments end-to-end (you will get the chance to learn these skills on the job). A day in the life might include:
Data collection and cleaning. Implementing scalable data pipelines
Designing processes and software to facilitate ML experimentation
Implementing and debugging new ML frameworks and approaches
Training models
Building tooling to evaluate and play with your models
Outside of modelling, you will also help with making your models come to life:
Improving a variety of things like data quality, data formatting, job startup speed, evaluation speed, ease of experimentation
Adjusting our infrastructure for model inference, such as improving constrained generation for tool-use
Working with engineering to integrate models into Proxy
Direct experience training LLMs or VLMs with methods such as distillation, supervised fine-tuning, and policy optimisation
Experience with large-scale distributed training and inference
Experience debugging ML systems and codebases
Proficiency in frameworks like PyTorch
Strong general foundations in software engineering, with an interest in non-ML software too – doing whatever it takes to build incredible models as a small team
Experience training Llama models or other open source models
Experience with frameworks for fine-tuning and RLHF
Familiarity with public datasets (including synthetic ones) for improving model capabilities
Experience with ML ops and infrastructure. Experience improving ML practices
Be at the cutting edge of AI and foundation models
Work on challenging problems that impact users' daily lives
Collaborative and innovative work environment
Opportunities for professional growth and learning
Competitive salary plus Equity
Benefits: 30 days PTO, Private Medical Cover, Pension, Wellness Benefit, Lunch Allowance and Flexible Working Environment
