Senior Machine Learning Engineer
Owns end-to-end production machine learning systems, including NLP, LLM, agentic, ranking, and recommendation capabilities. Requires 8+ years of industry experience, strong Python and cloud ML expertise, and the ability to deliver explainable AI products with cross-functional and customer impact.
About the job
Responsibilities
- Own machine learning problems end to end, from data exploration and modeling through deployment, monitoring, and production iteration.
- Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.
- Develop explainable ranking, recommendation, prediction, and optimization models.
- Partner with Product and Go-to-Market to turn ambiguous business problems into shipped capabilities.
- Improve the performance, scalability, and reliability of production ML systems and help shape AI platform architecture.
- Explain technical work to technical and non-technical audiences and engage with customers when needed.
- Mentor engineers and raise the bar for engineering excellence.
Requirements
- 8+ years of industry experience building and deploying machine learning systems in production, with clear end-to-end ownership.
- Strong foundation in machine learning and applied statistics, with hands-on depth in NLP, transformers, embeddings, and retrieval-based systems.
- Practical experience with modern generative AI tooling such as LangGraph, LangChain, or Amazon Bedrock.
- Strong Python skills and experience building distributed ML pipelines on cloud infrastructure such as AWS or Databricks.
- Solid understanding of feature engineering, model evaluation, and MLOps practices.
- Product mindset focused on building AI products customers use and measuring customer impact.
- Excellent communication and ability to explain complex technical work clearly to product and business partners.
- Comfort with ambiguity and judgment to drive execution independently.
Nice to Have
- Experience with RAG architectures, vector databases, and prompt engineering.
- Hands-on experience with PyTorch or TensorFlow.
- Background in B2B SaaS, enterprise AI products, or forward-deployed engineering, particularly involving complex customer data and rapid delivery.
Compensation and Benefits
- Base salary range: $200,349.50–$260,912.60.
- Additional compensation may include a bonus or commission plan and stock options.
- Benefits include health insurance, life and disability insurance, a 401(k) employer matching program, paid holidays, self-care days, paid time off, paid parental leave, stock options, equipment support, and learning and development programs.
Skills
Python, Machine Learning, Applied Statistics, Natural Language Processing, Transformers, Embeddings, Retrieval-Augmented Generation, LangGraph, LangChain, Amazon Bedrock, AWS, Databricks, MLOps, PyTorch, TensorFlow
Similar jobs
ML Engineering jobs
Traba
New York, NYBuild and deploy production AI-agent systems, including their harnesses, evaluations, orchestration, and supporting services. The role requires 5+ years of software engineering experience, production LLM or agent experience, and strong Python or TypeScript/Node.js skills.
Metriport
San Francisco, CA
Own machine learning end to end, from modeling messy clinical data through production deployment, monitoring, and infrastructure. The role requires 7+ years of experience building scalable ML systems, strong software and data engineering skills, and proficiency with Python, SQL, and cloud platforms.
SentiLink
United States
Leads and manages an applied machine learning team developing production fraud detection and identity verification models. The role combines people leadership with hands-on technical work and requires substantial ML experience, production deployment expertise, and experience in risk-focused domains.
Airbnb
United States
Builds and productionizes machine learning systems for trust and safety, including abuse detection, autonomous AI agents, and evaluation frameworks. The role requires 5+ years of applied ML experience, strong Python skills, experience with LLMs and scalable pipelines, and a relevant advanced degree or equivalent background.
Instacart
United StatesDevelop and deploy production machine learning models for real-time inventory and shelf-stocking intelligence at scale. The role requires 5+ years of production ML experience, strong Python and ML framework skills, cloud and data pipeline expertise, and a bachelor's degree or equivalent experience.