Applied ML Engineer Jobs

Our client, a fast-growing AdTech organization, is hiring a Machine Learning Engineer/Tech Lead to join their team in California. The successful candidate will build and scale advanced systems for prediction, recommendation and generative AI, as well be responsible for solving complex, large-scale machine learning challenges and delivering cutting-edge solutions.


Responsibilities

• Design, train and deploy ML models and large-scale systems for prediction, recommendation and content generation.

• Tackle advanced use cases including CTR/CVR prediction, ranking, fraud detection and cutting-edge generative methods (LLMs, transformers, diffusion models).

• Develop scalable algorithms combining deep learning, regression and hybrid rule-based techniques.

• Diagnose and resolve performance bottlenecks across infrastructure, tools and pipelines.

• Rapidly prototype, experiment and iterate on models using massive, real-world datasets.

• Guide and mentor junior engineers, and lead small technical projects when required.

• Leverage distributed computing and GPU environments to optimize model training and deployment.

Skillset

• Master’s or Bachelor’s degree in Computer Science, Engineering, Applied Sciences, Mathematics or similar.

• At least four years of experience in software engineering or applied Machine Learning.

• Proven record of building and deploying large-scale ML systems in production.

• Experience driving technical direction, mentoring teams or leading cross-functional projects.

• Strong foundation in algorithms, data structures and ML fundamentals.

Benefits

• Competitive compensation.

• Comprehensive benefits package.

• 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.

• 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.

• Salary: $190k - $225k DOE.
• Remote working.
• Comprehensive health, dental and vision coverage.
• Retirement benefits.