Principal Machine Learning Engineer

Bjak • Madison, MS

8d

About The Position

About A1
There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90% reduced time.
Role
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.

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.

Nice To Haves

• 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).

Responsibilities

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed