Principal Machine Learning Engineer

• Posted 19 hours ago • Updated 19 hours ago

Full Time

On-site

Fitment

Dice Job Match Score™

👾 Reticulating splines...

Job Details

Skills

FOCUS
Partnership
Leadership
Collaboration
Deep Learning
Transformer
PyTorch
JAX
Software Engineering
Optimization
Open Source
Computational Science
GPU
PPO
Data Processing
Apache HTTP Server
Apache Spark
Training
Evaluation
Root Cause Analysis
Machine Learning (ML)
Research
SAFE
Shipping
Expect
Value Engineering
Artificial Intelligence

Summary

{"description": " About the Role

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

As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company.

You operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems. While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.

This is a hands-on, high-impact role focused on depth.

Focus

Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.
Design reproducible, high-performance training pipelines across GPU infrastructure.
Architect inference systems that balance latency, throughput, cost, and reliability at scale.
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

Requirements

Strong background in deep learning and transformer-based architectures.
Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.
Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).
Strong software engineering fundamentals - you write robust, maintainable, production-grade systems.
Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
Comfort owning ambiguous, zero-to-one ML systems end-to-end.
A bias toward shipping, learning fast, and improving systems through iteration.

Ideal Experience

Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.
Contributions to open-source ML or systems libraries.
Background in scientific computing, compilers, or GPU kernels.
Experience with RLHF pipelines (PPO, DPO, ORPO).
Experience training or deploying multimodal or diffusion models.
Experience with large-scale data processing (Apache Arrow, Spark, Ray).

Outcomes

ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.
Models deployed to production achieve measurable quality improvements and meet user-impact goals.
Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.
Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.
Research-to-production cycles are efficient, safe, and continuously improve the product experience.

How We Work

The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team 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 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.", "salary_raw": null}

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90922487
Position Id: 24059643
Posted 19 hours ago

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