Skip to content

Machine Learning Engineer, Enterprise Brain

Develop ML systems for the Enterprise Brain, focusing on proactive AI for task prediction, automation, and agentic workflows using LLMs and advanced techniques. Requires 3+ years ML experience, Python proficiency, and expertise in evaluation and production systems.

200k – 300kPalo Alto, CASan Francisco, CAML EngineeringHybrid3+ YOE

About the role

Responsibilities

  • Work on deeply challenging ML problems involving user understanding and task prediction.
  • Invent new LLM workflows and signals to improve reasoning, planning, and personalization.
  • Design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction and other agentic systems.
  • Lead development of scalable evaluation, benchmarking, and optimization loops.
  • Build and maintain robust ML pipelines for enterprise and knowledge graph construction.
  • Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance.
  • Collaborate with cross-functional teams to deeply understand customer pain points and deliver high-quality, production-ready ML solutions.
  • Mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment.

Requirements

  • 3+ years of industry experience in AI or Machine Learning Engineering.
  • BA/BS in computer science, math, sciences, or a related field.
  • Experience with search, recommendation, natural language processing, or other large-scale ML systems.
  • Proven ability to design, build, and ship production-ready models and systems.
  • Demonstrated expertise in ML evaluation, benchmarking, and data quality—ideally with experience in building or maintaining evaluation frameworks for complex enterprise tasks.
  • Proficiency in your ML framework of choice (e.g., TensorFlow, PyTorch).
  • Strong coding skills (Python, Go, Java, C++, etc.).
  • Thrive in a customer-focused, cross-functional environment; a proactive and positive attitude is a must.

Compensation & Benefits

  • Base salary range: $200,000 - $300,000 annually (determined by location, level, knowledge, skills, experience).
  • Eligible for variable compensation, equity, and benefits.
  • Comprehensive benefits: Medical, Vision, Dental; generous time-off; 401k; home office stipend; education and wellness stipends; company events; daily lunches.

Skills

LLMsPyTorchTensorFlowPythonReinforcement LearningNatural Language ProcessingSearchRecommendation SystemsMl PipelinesEvaluation Frameworks

Similar roles

ML Engineering jobs

AI System Research and Development Engineer - Optimization

Develop and optimize GPU kernels and deep learning systems for LLM training and inference at Snowflake AI Research. Requires 5+ years in GPU/HPC optimization and strong proficiency in PyTorch, TensorFlow, JAX, and CUDA.

200k – 265kBellevue, WAML EngineeringOn-site5+ YOEJAXCUDA

Post-Training Research Engineer

Build in-house tooling for post-training custom ML models using advanced techniques like RL and finetuning. Requires deep expertise in transformer training, PyTorch distributed systems, parallelism strategies, GPU performance optimization, and HPC platforms.

200k – 275kSan Francisco, CAML EngineeringHybridJAXRay

Machine Learning Engineer, Images

Designs, fine-tunes, and deploys image generation models for photorealistic AI bots, optimizing for consistency, latency, and quality. Requires 5+ years software engineering, 2+ years production ML, and expertise in diffusion models like Stable Diffusion and PyTorch.

200k – 265kSan Francisco, CAML EngineeringRemote5+ YOEGCPAWS

Research Engineer, Core ML

Research Engineer building production ML systems at the intersection of efficient inference, RL/post-training, and serving engines. Translates algorithms into scalable infrastructure improving latency, throughput, and model quality. Requires 3+ years ML systems experience and advanced degree.

200k – 280kSan Francisco, CAML EngineeringOn-site3+ YOEDpovLLM

AI Engineer

Builds and deploys production-scale AI/ML systems using LLMs, from fine-tuning and evaluation to low-latency infrastructure. Requires 5+ years experience with PyTorch/TensorFlow, MLOps, AWS, and taking models to production at high-growth startups.

200k – 250kNew York, NYML EngineeringHybrid5+ YOERAGAWS