COMPANY A1
AI Engineering
PrincipalMachineLearningEngineer
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“Principal Machine Learning Engineer at COMPANY A1. Skills: Machine Learning, ML Systems, Inference Systems, Production Deployment. Turn research into ML systems. Build end-to-end ML pipelines”
What You'll Achieve.
Bring intelligence to conversations; Achieve high reliability; Complete real-world tasks
Industry & Context.
Multi-step reasoning; Troubleshooting
What They're Looking For.
Must Have
background in deep learning, transformer-based architectures, Hands-on experience training ML models, fine-tuning ML models, deploying ML models in production, Proficiency with PyTorch or JAX, software engineering fundamentals, Experience with GPU optimization, Comfort owning zero-to-one ML systems
Nice to Have
Experience with LLM inference frameworks, Contributions to open-source ML libraries, Background in scientific computing, Background in compilers, Background in GPU kernels, Experience with RLHF pipelines, Experience training multimodal models, Experience deploying multimodal models, Experience training diffusion models, Experience deploying diffusion models, Experience with large-scale data processing
What You'll Do.
Turn research into ML systems
Build end-to-end ML pipelines
Own end-to-end ML pipelines
Architect inference systems
Operate inference systems
Maintain data systems
Implement evaluation pipelines
Own production deployment
Improve memory efficiency
Collaborate with application engineering
Make pragmatic trade-offs
Ship improvements quickly
Learn from real usage
How You'll Work.
Team & Collaboration
Application engineering teams; Cross-functional teams
Process & Methodology
Execution
Full Job Description
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 real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. ROLE 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. FOCUS - 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 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). -
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