Senior Machine Learning Engineer

Define the ML strategy, raise the technical bar, and build the systems that scale. You are a systems thinker who turns ambiguous business problems into scalable ML architectures, serving as a bridge between research and engineering reality. You will have direct influence on the company’s AI roadmap. Responsibilities Architect end-to-end ML systems designed for scale, reliability, and long-term maintainability. Define and drive the ML technical roadmap in partnership with leadership. Lead research spikes on emerging techniques (LLMs, multimodal, RLHF) to determine production viability. Establish ML engineering best practices, including experimentation standards, model governance, and monitoring playbooks. Own the ML platform: feature store, training infrastructure, model registry, deployment pipelines, and observability stack. Mentor senior and mid-level engineers, conduct design reviews, and set coding standards. Represent ML engineering in cross‑functional forums, including product reviews, investor presentations, and customer discussions. Requirements Key Focus: Architect systems, lead team, and set strategy. Required Skills 10+ years of professional ML engineering or research engineering experience, with multiple large‑scale production deployments. Deep expertise in at least two ML domains (e.g., NLP, computer vision, recommendation systems). Hands‑on experience with large language models: pre‑training, fine‑tuning (SFT, RLHF, DPO), retrieval augmentation, and inference optimization. Expert‑level command of PyTorch and/or JAX; strong familiarity with the HuggingFace ecosystem. Track record of designing and owning ML platforms at production scale. Deep knowledge of distributed training (data parallelism, model parallelism, FSDP, DeepSpeed) and inference optimization. Demonstrated leadership: prior tech lead or staff engineer experience with direct mentorship. Exceptional communication skills, translating ML complexity for executives and customers. Valuable Experience (Nice to Have) Published research at top‑tier venues (NeurIPS, ICML, ICLR, etc.). Experience building ML teams from scratch or leading ML at a startup through a growth inflection. Background in MLSec, responsible AI, or model safety practices. Experience with hardware‑aware ML (CUDA kernels, TPU programming). #J-18808-Ljbffr