Machine Learning Jobs United States
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Our client, a leading Fintech company, are hiring a Staff Machine Learning Engineer to join their Applied AI team remotely. The successful candidate will be responsible for developing and deploying AI-powered multi-agent systems, human-in-the-loop co-pilots, specialized financial models and seamless legacy system integrations to automate processes, improve workflows and optimize existing financial technologies.
Responsibilities- Design and fine-tune both open source and proprietary large language models (LLMs) for tasks including summarization, reasoning, planning and question answering.
- Build and enhance advanced Retrieval Augmented Generation (RAG) pipelines featuring embedding fine-tuning, hybrid search, reranking and knowledge graph integration.
- Develop autonomous AI agent workflows that support proactive and adaptive decision-making.
- Utilize reinforcement learning methods (such as PPO, DPO, GRPO) to continuously improve model performance.
- Create evaluation frameworks and define metrics to rigorously assess model effectiveness.
- Deploy AI models into production environments with a focus on low latency, reliability and scalability.
- Work closely with product and engineering teams to deliver comprehensive AI-powered financial solutions.
Skillset- Master’s or Bachelor’s degree with at least 5 years of professional experience in applied AI/ML engineering.
- Demonstrated success in delivering generative AI products utilizing LLMs and autonomous agent workflows.
- Practical expertise with LLM fine-tuning methods (such as LoRA), inference frameworks (including vLLM) and sophisticated RAG pipelines.
- In-depth understanding of reinforcement learning fine-tuning techniques and associated frameworks.
- Early-stage startup experience is an advantage.
Benefits- Salary: $190k - $225k DOE.
- Remote working.
- Comprehensive health, dental and vision coverage.
- Retirement benefits.
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Our client, an exciting AI-driven company, are hiring a Machine Learning Engineer to join the team in Seattle, Washington or Palo Alto, California. The successful candidate will be instrumental in building and scaling the infrastructure behind the company’s open AI platform, leveraging strong platform engineering skills and a solid machine learning background to support everything from training workflows to production deployment.
Responsibilities- Develop and sustain reliable systems for training, deploying and scaling AI models.
- Build and enhance workflows for data preparation, model training, evaluation and deployment.
- Identify and resolve performance bottlenecks to ensure smooth operation for thousands of users and contributors.
- Automate infrastructure provisioning, deployment and monitoring following DevOps best practices.
- Collaborate closely with researchers and developers, both within the company and across our open-source community.
- Contribute to the development and direction of their open-source platform and foundational AI models.
Skillset- Extensive experience in infrastructure engineering, DevOps or cloud-native environments.
- Deep understanding of machine learning principles and practical experience with end-to-end ML workflows.
- Proficient in Python and skilled in best practices for software development.
- Practical knowledge of cloud platforms such as AWS, Google Cloud or Azure.
- Experience with distributed computing and designing scalable systems.
- Enthusiasm for open-source development and active participation in community-driven projects.
Benefits- Salary: $100K - $220K DOE.
- Equity.
- Comprehensive health, dental and vision insurance.
