Senior Machine Learning Engineer

Join Superlinked to build an ML framework for turning complex data into vector embeddings, that make vector-powered RAG, RecSys, Search & Analytics actually work. We raised a $9.5M Seed from Index Ventures and other San Francisco based investors, partnered with MongoDB, Redis, Starburst & Dataiku with many more in the pipeline. 

In this role you will be working directly with Eli Shtraikher, Head of Professional Services and the Superlinked team on building the product, making it work for our clients and partners and engaging with the developer community.

Your Role

You are a versatile software engineer with hands-on experience in software engineering and AI/ML applied research experience and strong communication skills, who can deliver results across a range of responsibilities:

• Owning an end-to-end R&D lifecycle: Ideation, research, evaluation of competing approaches, analysis, optimization, and productization, ensuring scalability and efficiency.
• Solution engineering: Talk to product / engineering people at a potential partner / customer, understand their needs and design a solution, using our and our partners products.
• Model building: Cover the full model lifecycle from exploratory data analysis, modeling and evaluation, to packaging and serving the model(s) through common frameworks & cloud platforms.
• Product: Collect market feedback and collaborate with the Framework and Infrastructure team to make sure our roadmap is focused on most valuable features.
• DevRel: Prepare materials and deliver talks at developer events, join panel conversations and podcasts, engage with our VectorHub Community.

We are a team of 20 now, mostly senior engineers - we don’t expect you to do all the above jobs alone all at once - but you should be reasonably comfortable with any of them as the situation requires. Our goal is to support you and help you grow across all these dimensions.

You

You have 5+ years of hands-on experience in software engineering and AI/ML research/development.

We expect you to be proficient in Python, FastAPI, mainstream relational databases with bonus for prior exposure to Ray, Kafka, Kubernetes, message queues, Redis / MongoDB / Qdrant / other vector DBs, more esoteric databases (graph, time series).

Exposure to LLMs is also quite important, you must have looked at least a few of: OSS model fine-tuning, prompt engineering frameworks like DSPy, structured output frameworks like instructor, serving frameworks like vllm, building Retrieval Augmented Generation with a vector database, cross encoder / ColBERT re-ranking.

Experience working on information retrieval systems (Search, RecSys, RAG) and the evaluation and tuning of their quality and performance is a huge advantage.

If you have managed people before, that is a plus, but not a must-have - we expect you to be very hands on initially and grow with the company as we scale.

Your experience and attitude

• You have built web-scale production data and/or ML systems, you have seen things fall apart and you brought them back together - from model drift to technical debt to organizational dysfunction.
• You believe that focused and empowered teams can deliver outsized results.
• You are collaborative by nature.
• You have a growth mindset and a passion for dissecting and solving complex problems.
• The idea of inventing a new kind of machine learning infrastructure product genuinely excites you.
• We don’t particularly care about which school you went to or what logos you worked for or what your identity is.

What we offer

• A competitive salary and ESOP package.
• A flexible and hybrid work environment.
• A chance to work on a groundbreaking project with a talented and passionate team.
• An opportunity to shape the future of vector compute and AI infrastructure.