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Job Category
Software Engineering
Job Details

ABOUT SALESFORCE

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we’re looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce’s core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

ABOUT US

In June 2025, Convergence AI was acquired by Salesforce, joining the Agentforce team. The goal is to establish Salesforce’s first engineering team in London, focusing on agent research topics and developing the next generation of personal assistants capable of automating large, previously labor-intensive workstreams for the enterprise world.

REQUIREMENTS

• Deep knowledge of transformer architectures and vision-language models and strong theoretical understanding of deep learning fundamentals
• Expertise in training and fine-tuning open source models using techniques such as supervised fine-tuning or reinforcement learning
• Experience with large-scale distributed training and inference using e.g. DeepSpeed, FSDP, Ray
• Proficiency in PyTorch and related frameworks

BONUS QUALIFICATIONS

• Publication record in top-tier ML conferences (NeurIPS, ICML, ICLR) or top journals
• Contributions to open-source ML frameworks
• Experience developing novel datasets or data generation approaches
  Unleash Your Potential
  When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
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  We are a recognised Disability Confident member under the UK Government Disability Confident employer scheme. We are committed to providing an inclusive recruitment process and will offer an interview to disabled applicants who meet the essential criteria for the role. Applicants are welcome to opt-in to the interview scheme as part of the application process. If you would like to apply under the scheme, please click the link to the Accommodations Request Form above and scroll to the UK Disability Confident Scheme section within the form.
  Posting Statement
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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 model

THE ROLE

We are looking for talented ML research engineers and researchers to join our team and focus on training models which power our brand new enterprise 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:

• Implementing and testing different fine-tuning and preference learning techniques like GRPO, PPO, and DPO.
• Building datasets through 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 production teams to integrate models