Bjak
Senior Technical Product Manager - AI Agents, Evals & Reliability

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About A1
There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.
Role
This is a deeply technical, hands-on role. Work directly with engineers on system design, evaluation, and trade-offs-defining requirements, but shaping how the system works for global users. You work at the intersection of user needs, model capability, and system constraints, and are responsible for turning AI potential into real, reliable behavior in a real-world application.
What You'll be Doing
- Research and define end-to-end AI system requirements from capability to behavior to user impact
- Translate model capabilities, data constraints, and evaluation results into clear product and system decisions
- Make hard trade-offs across quality, latency, cost, reliability, and UX
- Work closely with ML, backend, and mobile engineers on system design, evaluation, and iteration
- Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback
- Drive execution with clear specs, strong judgment, and disciplined prioritization
- Ensure systems ship quickly, safely, and reliably, with strong feedback loops
- Own product quality end-to-end - correctness, predictability, and user trust
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What You Will Need
Technical foundation
- Strong grounding in computer science fundamentals, including algorithms, data structures, and system design.
- Solid understanding of ML fundamentals and how modern AI systems behave in production.
- Comfort reading, reviewing, and discussing technical design documents.
AI & ML experience
- Hands-on exposure to AI-powered products, including LLM-based systems.
- Experience working with model evaluation, prompt or pipeline iteration, and feedback loops.
- Strong intuition for model limitations, hallucinations, bias, and drift.
Product leadership
- Significant experience owning complex, technical products end-to-end.
- Proven ability to work closely with senior engineers and ML teams.
- Strong judgment and decision-making ability in ambiguous, fast-moving environments.
- Ability to balance ambition with technical and operational reality.
Nice to have
- Experience shipping AI-heavy consumer products.
- Background as an engineer or highly technical product manager.
- Experience defining evaluation metrics for ML systems.
- Strong intuition for AI UX patterns and failure handling.
- Prior experience in zero-to-one product environments.


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Outcomes
- Product strategy clearly aligns AI capabilities with user needs and company priorities.
- AI features deliver real value, are understandable, predictable, and trusted by users.
- Decisions balance quality, speed, cost, and reliability effectively under uncertainty.
- Roadmaps and priorities are clear, with fast iteration based on real user feedback.
- Teams are aligned, focused, and able to execute on AI product goals with minimal friction.
How We Work
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning.
Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product.
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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