Member of Technical Staff, Machine Learning

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.

Originally posted on Himalayas