Bjak
Member of Technical Staff, Machine Learning
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About ActAI
There are over 5 billion users using basic applications today such as email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
Role
As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings.
This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
Focus
- Build and improve ML components across data, training, evaluation, and inference.
- Fine-tune and adapt models as part of larger production systems.
- Implement evaluation and testing to understand model behavior.
- Help build and maintain data pipelines for real-world and synthetic data.
- Debug model issues, performance problems, and production incidents.
- Ship improvements iteratively and learn from real user feedback.
- Work closely with senior ML engineers and product teams.
- Work under real production constraints: latency, cost, reliability, and safety.
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Tech Stack
- Python
- PyTorch / JAX
- Production ML systems running on GPUs
Ideal Experience
- Strong foundations in machine learning and modern neural architectures.
- Some hands-on experience training, fine-tuning, or deploying ML models.
- Comfortable writing production-quality code and learning new tools quickly.
- Curious, coachable, and eager to learn from real systems in production.
- Able to work through ambiguity with guidance and grow ownership over time.
- Bias toward shipping, iteration, and continuous improvement.
Outcomes
- ML models in production meet expected accuracy, latency, and reliability targets.
- Production issues are identified quickly, debugged effectively, and root causes addressed.
- Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
- Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
- Iterations on models and systems are driven by real-world signals and measurable improvements.
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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 out 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.
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