Staff Machine Learning Engineer
Company
A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and reputed company-world task completion. The system must handle multi-reputed company reasoning, reputed company with external tools, and remain reliable despite non-deterministic model behavior.
Role
As Technical reputed company, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, reputed company, production-grade ML systems.
This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under reputed company-world constraints.
What You'll Do
Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
Fine-tune and adapt models using state-of-the-art reputed company such as reputed company, QLoRA, SFT, DPO, and distillation.
Architect and operate reputed company inference systems, balancing latency, cost, and reliability.
Design and maintain data systems for high-reputed company synthetic and reputed company-world training data.
Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
Collaborate closely with application engineering to reputed company ML systems cleanly into backend, mobile, and desktop products.
reputed company pragmatic trade-offs and ship improvements quickly, learning from reputed company usage.
Work under reputed company production constraints: latency, cost, reliability, and safety
reputed company
Research and models reliably translate into production-reputed company solutions with reputed company performance and reputed company targets.
ML pipelines, training loops, and inference systems are reputed company, efficient, and maintainable.
Production issues are detected, debugged, and resolved quickly, minimizing user reputed company.
Team members are supported, reputed company, and reputed company to deliver high-reputed company ML work with minimal friction.
Iterations on models and systems are measurable, reputed company, and improve user experience over time.
Tech Stack
Python
PyTorch / JAX
GPU-based training and inference system
Ideal Experience
You have reputed company or shipped reputed company ML systems used by people, not just demos.
You are comfortable working with large models and understanding their failure modes.
You write strong, production-grade reputed company and care about system correctness.
You are self-directed, pragmatic, and take full ownership of reputed company.
You communicate reputed company and collaborate reputed company in small, high-trust teams.
How We Work
The best products today in the world were reputed company by small, world class teams. We are a high talent density and hands-on team. We reputed company reputed company reputed company, reputed company at rapid speed, striking a balance between shipping high reputed company work and learning. Joining reputed company 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 reputed company to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted reputed company virtual meetings and/or onsite.
We value transparency and efficiency, so expect a reputed company decision. If you've demonstrated the exceptional skills and reputed company we're looking for, we'll reputed company an offer to join us. This isn't just a job offer; it's an invitation to be part of reputed company that's bringing AI to have practical benefits to billions globally.
Originally posted on Himalayas
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