Lead ML Engineer

Lead Machine Learning Engineer – New York Location: New York City (Hybrid – 3 days/week onsite)We’re working on behalf of a deeply technical, research-driven hedge fund operating at the frontier of computational science, quantitative finance, and AI. This is a rare opportunity to join a small, exceptionally high-caliber team applying state-of-the-art machine learning to real-world, high-stakes systems — with significant technical and financial upside.This is not a product company. This is not ad tech.You’ll be working alongside researchers and engineers with backgrounds spanning theoretical physics, comp bio, algorithmic trading, and large-scale ML systems, solving problems where complexity, precision, and scale intersect.What You’ll Be DoingLeading the design, training, and optimization of advanced machine learning models (e.g., foundation models, graph-based methods, or custom architectures)Building production-grade pipelines that support data ingestion, model training, evaluation, and inference at scaleCollaborating with domain experts and quants to translate research into deployed, high-performance systemsContributing to core infrastructure for model deployment, experimentation, and monitoringStaying deeply informed on the latest ML research — and applying breakthroughs where they provide real-world advantageWhat They’re Looking For6+ years experience in applied ML / deep learning, with strong research intuitionExpert-level knowledge of Python and ML frameworks (e.g. PyTorch, TensorFlow, JAX)Deep experience with modern ML architectures — transformers, GNNs, generative models, etc.Strong mathematical grounding (linear algebra, optimization, probabilistic modeling)Track record of shipping high-impact ML systems in production environmentsExperience owning projects end-to-end — from model development to deployment at scalePreferred (but not required)PhD or Master’s in CS, Math, Physics, Statistics, or similarPrior experience in quantitative finance, computational research, or scientific computingExperience with distributed systems, HPC environments, or low-latency ML deploymentContributions to ML research, open-source, or publications in top-tier conferences (NeurIPS, ICLR, ICML, etc.)Why This RoleWork at the intersection of cutting-edge AI and applied quantitative researchCollaborate with one of the most selective technical teams in the industryHigh autonomy, zero bureaucracy, and meaningful ownershipCompetitive compensation + substantial performance-based upsideHybrid in NYC — collaborative environment with world-class peersIf you’re among the top 1% of ML engineers and looking for an environment that values technical depth, scientific rigor, and real-world impact - we’d love to speak with you.Apply now or reach out directly for a confidential conversation.