Principal Machine Learning Engineer - Palo Alto - A1

    A1
    A1 Palo Alto

    4 days ago

    Description

    Overview

    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.

    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).
    • Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.
    • Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
    • 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).

    Compensation Range

    $347K – $490K

    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.

    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.


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