At a Glance
- Tasks: Lead the execution layer for reliable and capable AI agents in a dynamic environment.
- Company: Join a cutting-edge tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Engage with enterprise clients and drive real-world solutions in a fast-paced setting.
- Why this job: Shape the future of AI by managing impactful agent systems and collaborating with top talent.
- Qualifications: 5+ years in product management with expertise in AI systems and engineering.
The predicted salary is between 70000 - 90000 € per year.
Requirements
- 5+ years of product management experience in agentic AI systems, developer infrastructure, or applied ML products
- Deep understanding of modern LLM agent architectures, including multi-agent systems, tool-augmented reasoning, memory and retrieval, programmatic orchestration, RAG, and long-horizon execution
- Strong grasp of agentic evaluation design, including how to measure task completion, failure recovery, and long-horizon reliability, and how to diagnose model vs. scaffolding gaps
- Technically deep enough to contribute to architecture decisions at the implementation level: comfortable reviewing and shaping design docs, reasoning about async execution patterns, sandboxed environments, filesystem design, and the tradeoffs that come with building harness capabilities into a production platform
- Ability to flex between ML research conversations and engineering architecture discussions with equal fluency
- Track record of shipping platform-layer products with demonstrated impact on reliability, performance, or capability
- (Desirable) An active practitioner of agent frameworks who regularly builds with and follows the latest developments in open-source harnesses, coding agents, and orchestration tools in both professional and personal work
- (Desirable) Hands-on experience with enterprise agentic deployments: multi-tenant orchestration, tool permissioning, audit trails, and compliance requirements
- (Desirable) Familiarity with infrastructure constraints relevant to enterprise deployments: on-premises environments, scalability challenges, and the operational tradeoffs of running complex agent workloads in restricted or air-gapped settings
- (Desirable) Prior work at the intersection of research and product, translating nascent model capabilities into shipped product features
- (Desirable) Background working within or closely alongside an ML research or post-training team
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!
What the job involves
We are seeking an Agent Harness Product Manager to own the execution layer that makes North agents reliable, capable, and production-ready. This is a role that sits at the intersection of three domains:
- Agent Loop and Execution: Own the core agent runtime: tool orchestration, parallel execution, sub-agent delegation, sandbox code execution, and failure recovery. You will define how North agents plan and act across long, multi-step workflows and ensure the execution environment is robust enough for the most demanding enterprise tasks. You are expected to engage at the implementation level, contributing to architecture decisions alongside engineering rather than simply handing off requirements.
- Context Engineering: Own how our Agents manage the context window as a deliberately controlled resource. This includes progressive disclosure of tools and skills, context compaction and summarization, offloading of large payloads to a persistent filesystem, and the instrumentation that keeps agents oriented across extended trajectories.
- Model-Scaffolding Co-evolution: Own the feedback loop between North's harness and the Modeling Team. This PM is the connective tissue that makes that possible: ensuring harness design decisions are validated by Modeling before they are built, that evals are the shared bridge between both teams, and that as the harness evolves the model evolves with it.
Define and own the roadmap for North's agent harness, including the agent loop, context engineering layer, tool orchestration, sandbox execution, and sub-agent delegation. Serve as the primary interface between North engineering and Cohere's Modeling team, ensuring new harness capabilities validated before being built and that neither team paints itself into a corner. Own North's agentic evaluation framework, ensuring evals are compatible with both the North harness and Modeling's training infrastructure, and that they serve as a reliable bridge between product and research. Engage enterprise customers to surface real-world agentic failures and translate findings into concrete product and model requirements. Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that keep North's architecture aligned with emerging standards.
Product Manager (Agent Harness & Modelling) in London employer: Deepstreamtech
As a leading innovator in agentic AI systems, we pride ourselves on fostering a collaborative and dynamic work environment that encourages creativity and technical excellence. Our commitment to employee growth is reflected in our robust training programmes and opportunities for cross-functional collaboration, ensuring that every team member can thrive and contribute meaningfully to cutting-edge projects. Located in a vibrant tech hub, we offer competitive benefits and a culture that values diversity, making us an exceptional employer for those looking to make a significant impact in the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Product Manager (Agent Harness & Modelling) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for those interviews by practising common questions and scenarios related to product management in AI systems. We recommend doing mock interviews with friends or using online platforms to get comfortable with your responses.
✨Tip Number 3
Showcase your passion for agentic AI! Bring examples of your work or projects that highlight your experience with multi-agent systems and tool orchestration. This will help you stand out and demonstrate your hands-on knowledge.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Product Manager (Agent Harness & Modelling) in London
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your 5+ years of product management experience, especially in agentic AI systems or applied ML products. We want to see how your background aligns with our needs, so don’t hold back on the details!
Get Technical:We’re looking for someone who can dive deep into architecture decisions. If you’ve got experience with LLM agent architectures or tool orchestration, make it clear in your application. This is your chance to show us you can handle the technical side of things!
Connect the Dots:Your application should reflect how you can bridge the gap between ML research and product management. Share examples of how you've translated model capabilities into shipped features. We love seeing that connection in action!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your knowledge of agentic AI systems and modern LLM architectures. Be ready to discuss specific examples from your experience that demonstrate your understanding of multi-agent systems and tool-augmented reasoning.
✨Showcase Your Impact
Prepare to talk about your track record in shipping platform-layer products. Highlight how your contributions have improved reliability, performance, or capability in previous roles. Use metrics and concrete examples to illustrate your impact.
✨Engage in Technical Discussions
Be ready to dive deep into technical conversations about architecture decisions. Familiarise yourself with async execution patterns and filesystem design, as well as the trade-offs involved in building harness capabilities. This will show your fluency in both product management and engineering.
✨Connect with the Team
Demonstrate your ability to bridge the gap between ML research and product management. Prepare to discuss how you've collaborated with engineering teams in the past and how you plan to ensure that harness design decisions are validated by the modelling team before implementation.
