The posting
About the Client
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As a Member of Technical Staff, Machine Learning, you'll build core ML components and 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.
What You'll Work On
- 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
Requirements
- 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 production systems
- Able to work through ambiguity with guidance, growing ownership over time
- A bias toward shipping, iteration, and continuous improvement
Tech Stack
Python · PyTorch / JAX · production ML systems running on GPUs
What Success Looks Like
- ML models in production meet accuracy, latency, and reliability targets
- Production issues are found quickly, debugged effectively, and root-caused
- Data pipelines, training loops, and inference systems stay robust, reproducible, maintainable
- Effective collaboration with engineers, product, and research to ship reliable ML-powered features
- Iteration is driven by real-world signals and measurable improvement
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision. To be considered, please make sure to answer the screening questions in the application form — we're not able to review submissions that skip them.
