Machine Learning Engineer (London)

Machine Learning Engineer

About Us

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.

The Role

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.

Responsibilities

Your role will span the full stack of model training, including:

1. Implementing and testing different fine-tuning and preference learning techniques like DPO
2. Building datasets through scrappy methods, including synthetic data pipelines, data scrapers, combining open source datasets, and spinning up data annotations
3. 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:

1. Data collection and cleaning. Implementing scalable data pipelines
2. Designing processes and software to facilitate ML experimentation
3. Implementing and debugging new ML frameworks and approaches
4. Training models
5. Building tooling to evaluate and play with your models

Outside of modelling, you will also help with making your models come to life:

1. Improving a variety of things like data quality, data formatting, job startup speed, evaluation speed, ease of experimentation
2. Adjusting our infrastructure for model inference, such as improving constrained generation for tool-use
3. Working with engineering to integrate models into Proxy

Requirements

1. Direct experience training LLMs or VLMs with methods such as distillation, supervised fine-tuning, and policy optimisation
2. Experience with large-scale distributed training and inference
3. Experience debugging ML systems and codebases
4. Proficiency in frameworks like PyTorch
5. 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

Bonus Qualifications

1. Experience training Llama models or other open source models
2. Experience with frameworks for fine-tuning and RLHF
3. Familiarity with public datasets (including synthetic ones) for improving model capabilities
4. Experience with ML ops and infrastructure. Experience improving ML practices

Why Join Us?

1. Be at the cutting edge of AI and foundation models
2. Work on challenging problems that impact users' daily lives
3. Collaborative and innovative work environment
4. Opportunities for professional growth and learning
5. Competitive salary plus Equity
6. Benefits: 30 days PTO, Private Medical Cover, Pension, Wellness Benefit, Lunch Allowance and Flexible Working Environment

#J-18808-Ljbffr