Member of Technical Staff (Post-training)

Salesforce, Inc..; London; On-site; GBP 65,000 - 95,000; Full time

Job summary

A leading technology company in London is seeking talented ML research engineers to focus on training models that power a new enterprise agent. This role offers the chance to work with cutting-edge technology, including multi-modal vision models, while also laying the foundations for machine learning engineering practices. Ideal candidates will have deep knowledge of transformer architectures and expertise in PyTorch.

Qualifications

• Deep knowledge of transformer architectures 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.

Responsibilities

• Implementing and testing different fine-tuning and preference learning techniques.
• Building datasets through synthetic data pipelines.
• Conducting experiments to find good data mixes and hyperparameters.
• Data collection and cleaning.
• Implementing scalable data pipelines.

Skills

Deep knowledge of transformer architectures

Strong theoretical understanding of deep learning

Expertise in training open source models

Experience with large-scale distributed training

Proficiency in PyTorch

Job description

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.

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

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