Founding Lead Machine Learning Engineer

machine learning

natural language

Tensorflow

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About Us

We are on a mission to bridge the gap between enterprise business knowledge and data, democratizing data discovery and curation to prepare organizations for the era of generative AI. Today's data tools are overly complex, poorly integrated, and siloed, forcing AI Practitioners and data scientists alike to spend more time wrestling with tools, relying on tribal knowledge, and navigating data lakes rather than doing meaningful data science work. The current landscape of data tools and processes is heavily manual and needs to catch up with the vast amount of data generated daily. With the advent of Gen AI and multi-modality, this challenge has only grown more complex and broken.

Backed by top VC funds, we are committed to making enterprise data AI-ready faster, more reliably, and with a stronger foundation of factual semantic knowledge. This leads to more accurate models, superior outcomes, and better business results. Our team of seasoned data infrastructure and machine learning experts (from LinkedIn, Visa, Truera, Hive, and Branch) has spent the past two decades building bespoke systems to solve these very challenges.

Join our growing team of ML research and data infrastructure experts. We're committed to empowering AI and data scientists to seamlessly integrate semantic learning with generative AI. Be part of our journey to shape the future of enterprise AI.

About the job

We’re looking for a Founding Machine Learning Engineer to lead the development of our AI-driven knowledge discovery systems. This role is for someone who thrives in early-stage, high-impact environments, shaping both the technical foundation and strategic direction of our ML efforts. As a founding team member, you’ll take ownership of designing and deploying cutting-edge models and pipelines for Knowledge Extraction, Natural Language Understanding (NLU), and Information Retrieval while driving advances in unsupervised learning, and fine-tuning LLMs to build scalable, high-performance AI applications.

This role demands deep technical expertise and a builder mentality—someone who can move fast, make key architectural decisions, and iterate hands-on. You’ll work alongside the founders to shape the product roadmap, hire the ML team, and set the long-term AI vision, balancing rapid development with long-term scalability.

What You Will be Doing

Design and build state-of-the-art NLP and information retrieval systems to extract knowledge from vast, unstructured datasets.

Fine-tune and optimize LLMs (Large Language Models) for semantic search, knowledge discovery, and adaptive reasoning.

Build and maintain high-performance data pipelines for ingesting, processing, and serving structured and unstructured data.

Develop methods to build and evaluate AI Data Graphs

Lead and mentor engineers, fostering a hands-on, execution-driven culture.

Collaborate with cross-functional teams to align AI models with real-world applications and business objectives.

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Prior Experience

8+ years of experience in software engineering, with a strong focus on Machine Learning, NLP, and Information Retrieval.

Deep expertise in Natural Language Understanding (NLU), Knowledge Extraction, and Unsupervised Learning.

Experience in LLM fine-tuning, parameter-efficient tuning methods (LoRA, adapters), and retrieval-augmented generation (RAG).

Strong background in search and ranking systems, with expertise in semantic search, vector databases, and hybrid retrieval approaches.

Hands-on experience with transformer architectures, tokenization strategies, and embedding models (BERT, T5, GPT, etc.).

Proficiency in Python, PyTorch, TensorFlow, and familiarity with distributed training and model optimization techniques.

Experience designing scalable ML pipelines using Kafka, Spark, Ray or distributed compute frameworks.

Strong understanding of evaluation metrics for LLMs, search relevance, and user engagement optimization.

Prior experience leading teams and driving AI research from ideation to production.

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Nice to Haves

Experience working with enterprise-scale LLM applications, knowledge retrieval, and AI-powered data governance.

Familiarity with contrastive learning, self-supervised learning, and multi-modal AI.

Background in long-context modeling and efficient memory retrieval for LLMs.

Experience working with ray,vllm

machine learning

AGI

RAG