Staff Machine Learning Engineer

Bjak; New York (NY); On-site; USD 100,000 - 140,000; Full time

Job summary

A forward-thinking tech company in New York is looking for an ML Engineer to build and own end-to-end ML pipelines. The role involves optimizing GPU-based systems and collaborating closely with application engineers. Ideal candidates should have experience with real ML systems, a strong coding background, and the ability to work within production constraints. Join us to contribute to transformative AI solutions that can benefit billions globally.

Qualifications

• Experience building or shipping real ML systems used by people.
• Comfortable working with large models and understanding their failure modes.
• Strong coding skills and attention to system correctness.

Responsibilities

• Build and own end-to-end ML pipelines for data, training, evaluation, inference, and deployment.
• Fine-tune models using state-of-the-art methods.
• Architect scalable inference systems balancing latency, cost, and reliability.

Skills

Building real ML systems

Working with large models

Production-grade code

Collaboration in teams

Tools

Python

PyTorch

JAX

Job description

About the Role

A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.

You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment.

What You'll Do

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.

• Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.

• Architect and operate scalable inference systems, balancing latency, cost, and reliability.

• Design and maintain data systems for high-quality synthetic and real-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 integrate ML systems cleanly into backend, mobile, and desktop products.

• Make pragmatic trade-offs and ship improvements quickly, learning from real usage.

• Work under real production constraints: latency, cost, reliability, and safety

Tech Stack

• Python

• PyTorch / JAX

• GPU-based training and inference system

Ideal Experience

• You have built or shipped real 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 code and care about system correctness.

• You are self-directed, pragmatic, and take full ownership of outcomes.

• You communicate clearly and collaborate well in small, high-trust teams.

How We Work

Our organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands‑on and to contribute directly to the company’s mission.

Interview process

If there appears to be a fit, we’ll reach 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.