About Us

MindsDB is a fast-growing AI startup headquartered in San Francisco, California. As a leading innovator bringing AI and Data together, our passion is empowering companies to easily build AI capabilities that can Think, Understand and Orchestrate: enabling teams to move from prototyping & experimentation to production in a fast & scalable way.

MindsDB was founded in 2017 by Adam Carrigan and Jorge Torres, inspired by Ian M. Banks's Culture series, in which super AI systems called Minds collaborate with other forms of life to accomplish incredible goals. Starting as an Open-Source project, MindsDB has grown to be one of the most widely used AI-Data platforms in the world, with a growing community and more than 700 contributor developers from every corner of the globe.

We are backed with over $55M in funding from Mayfield, Benchmark, YCombinator, and nVidia. MindsDB is also recognized by Forbes as one of America's most promising AI companies (2021) and by Gartner as a Cool Vendor for Data and AI (2022).

THE ROLE

As a Machine Learning Engineer you'll focus on building advanced machine learning solutions for the MindsDB platform, including robust Text-to-SQL systems and optimizing Retrieval Augmented Generation (RAG) for both structured and unstructured data. Your expertise in transformer models and advanced retrieval techniques will be essential in delivering state-of-the-art LLM-driven solutions.

In this role, you'll fine-tune and deploy transformer models like Llama and OpenAI APIs, while collaborating with cross-functional teams to solve complex problems. Your experience in data structures, algorithms, and software design will help you contribute to innovative AI solutions at MindsDB, where you'll also have the opportunity to grow your skills in MLOps and model deployment.

This is a hybrid role (2-3 days in office/week) and we are looking for a candidate based in the Bay Area.

What You'll Be Working On

Researching, building, and evaluating novel LLM-powered enterprise applications.
Developing robust Text-to-SQL systems for interacting with enterprise data sources.
Building and maintaining Retrieval Augmented Generation (RAG) systems for diverse data sources, and designing and optimizing retrieval systems for both structured and unstructured data. Experience with advanced RAG algorithms beyond naive approaches is required.
Researching and implementing advanced chunking techniques (e.g., semantic, contextual retrieval, late chunking) along with a thorough understanding of retrieval concepts such as embeddings, late interactions, query expansion, bi-encoders, and cross-encoders. Apply these techniques based on specific application needs.
Building agentic and tool-calling systems to extend the capabilities of LLMs.
Employing an "Evaluation Driven Development" approach, working with messy datasets and creating evaluation metrics.
Fine-tuning and deploying transformer models (e.g., Llama, OpenAI APIs), building agent-based applications, and integrating them into production environments.
Be capable of building a RAG system from scratch without relying on LLM frameworks, while being familiar with LLM frameworks (e.g., Langchain, LlamaIndex, DSPy) or willing to learn.
Demonstrating strong skills in data structures, algorithms, concurrency, multi-threading, and design patterns. Write clean, maintainable code.
Collaborating closely with engineers and researchers, emphasizing team collaboration, documentation, and best engineering practices. Work with cross-functional teams, manage pull requests, and participate in code reviews.
Creating design documents, technical specifications, and lead architecture discussions.

Requirements/Qualifications

You will have:

3+ Years of ML Engineering Experience
Proven experience in machine learning engineering, particularly with LLMs and retrieval-based systems.
Strong software engineering skills, including experience in data structures, algorithms, and software design.
Experience working with transformer models, fine-tuning, and deploying them in production.
Ability to build end-to-end machine learning systems, especially in RAG and agentic contexts.
Familiarity with LLM frameworks or a willingness to learn.
Excellent problem-solving abilities and a passion for creating innovative AI-driven solutions.
Strong communication and team collaboration skills.

Nice to have:

Startup experience with fast-paced adaptability.
Cloud platform experience (AWS, GCP, Azure) for ML deployments.
MLOps knowledge, including CI/CD and model monitoring.
Experience with big data tools (SQL, NoSQL, Spark).
Open-source contributions in LLM or AI projects.

Benefits & Perks

Flexible Working Hours
Competitive Compensation
Competitive Medical, Dental, Vision, Life Insurance
401k with up to 6% matching
Unlimited PTO
New Hire Remote Setup budget ($1500)
Lunch Provided Mon-Fri
Internet Budget ($25/month)
Commuter Budget ($1200/year)
Learning & Development budget
Wellbeing Budget ($1200/year)
Monthly (virtual) team events
International in-person company retreats
Wellbeing/Mental Health leave

DIVERSITY, EQUALITY & INCLUSION

MindsDB is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all of our employees. MindsDB will give all qualified applicants consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.

Salary Range

$200,000—$240,000 USD

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