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).