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DNA325
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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.
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.
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
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
Python · PyTorch / JAX · production ML systems running on GPUs
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
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.
Originally posted on Himalayas
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