Applied ML Engineer Jobs

Our client, a leading FinTech organization, are hiring a Principal Machine Learning Engineer to join their Applied AI team in New York. The successful candidate will take technical ownership of a key product line, specializing in generative AI solutions that leverage large language models (LLMs) at their core.


Responsibilities

• Shape and execute the company’s AI strategy to drive continuous innovation and business growth.

• Work closely with product and engineering teams to develop comprehensive, end-to-end AI-powered products.

• Design and implement multi-agent systems that automate workflows and enhance operational efficiency.

• Develop AI-driven co-pilot tools to optimize advisor processes such as prospecting, conversion, onboarding and client servicing.

• Build tailored models to manage complex financial interactions, delivering fast, actionable insights.

• Create innovative solutions to seamlessly integrate and optimize legacy systems with modern AI technologies.

• Lead rapid zero-to-one product development cycles, including prototyping, testing and feature integration.

• Design and fine-tune both open-source and proprietary large language models for applications including question answering, summarization, reasoning and planning.

• Employ reinforcement learning methods to continually enhance model accuracy and performance.

Skillset

• Master’s or PhD degree in Computer Science, Engineering or similar.

• At least 7 years of experience in applied AI engineering, including at least 2 years in a technical leadership capacity.

• Demonstrated success delivering generative AI products based on large language models.

• Expertise in creating agentic workflows that enable autonomous AI functions.

• Previous experience in early-stage startups or building products from the ground up.

Benefits

• Salary: $200,000 - $250,000

• Equity.

• Comprehensive health, dental and vision insurance.

• Retirement benefits.

• 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.

Our client, an AI startup in the music industry, is hiring an Applied Machine Learning Engineer to join the team in California. The successful candidate will combine their expertise in Machine Learning engineering and software development to build intelligent tools for music creation and streamline complex audio workflows through automation.


Responsibilities

• Develop and apply machine learning algorithms to elevate their music creation tools and address real user needs.

• Leverage both ready-made and custom ML solutions to deliver impactful, efficient results.

• Ensure solutions are production-ready through domain shift testing, QA processes, A/B experiments and reliable deployment strategies.

• Write clean, scalable, and maintainable code with a focus on enhancing product performance and user experience.

• Design and manage robust data pipelines for processing audio and other unstructured data types.

• Collaborate closely with Product and Engineering teams to integrate ML models seamlessly into the platform.

• Fine-tune, evaluate and deploy pre-trained models for tasks such as audio analysis, melody generation and workflow automation.

• Advocate for ethical and responsible AI practices, prioritizing fairness, transparency and positive user outcomes.

Skillset

• Solid experience in software development using Python.

• Strong track record of implementing machine learning models, particularly using PyTorch.

• Hands-on experience deploying ML models in production environments; familiarity with AWS is a bonus.

• Comfortable handling unstructured data, especially audio.

• Strong aptitude for applied problem-solving, with a focus on quick, effective integrations.

• Familiarity with generative AI architectures such as transformers, large language models (LLMs), or diffusion models.

• A background or interest in music, audio production, or music technology.

• Excellent communication skills with ability to work seamlessly with both technical and non-technical team members.