> Markdown version of [/jobs/ext/4163990-applied-research-continual-learning](https://www.wearedevelopers.com/jobs/ext/4163990-applied-research-continual-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Research - Continual Learning - **Company:** Prime Limited - **Location:** London, UK (Remote available) - **Salary:** £150,000.0 - £300,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Software Debugging, Python (Programming Language), Open Source Technology - **Published:** October 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=00d4b24a5e05c52f ## About the Role * Relevant experience: continual or online learning, RL, meta-learning, memory systems, post-training data loops, adaptive production ML, or evaluation under changing distributions. * Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops. * Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes. * Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend. * Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them. Evidence of exceptional work We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach. ## Description You will build continual learning systems that turn agent trajectories, corrections, and failures into new tasks and model updates. You will compare streaming RL, batched post-training, and distillation, and determine which improvements belong in model weights or persistent memory, skills, prompts, and subagent state. Your experiments will track skill acquisition, transfer, forgetting, and loss of plasticity over long sequences of tasks. Responsibilities Applied Research at Prime We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities. Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda. Role scope Control update cadence, policy lag, task drift, and exploration, and evaluate each checkpoint on earlier skills, unseen tasks, and general capabilities. Build task-generation and training pipelines with reproducible data and harness versions, so regressions can be traced to a specific change and rolled back. You will work directly with the external collaborators whose deployments drive the research. Representative early problems * Select high-value production traces and interventions, convert them into training signal, and establish regression gates before a change reaches deployment. * Determine which adaptation belongs in model weights, harness state, memory, tools, or data, then measure the tradeoffs across a sequence of tasks. * Build evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift. What you will own * Methods for selective consolidation, replay, adaptation, memory, and online or offline RL. * Evaluations for retention, transfer, regression, and rollback as tasks and distributions change. * Promotion gates and human oversight for agents that continue to change in production. * Field experiments, benchmarks, and open research that make continual improvement measurable and transferable.