Top AI & Machine Learning Jobs

The Senior Data Scientist will lead analytical projects, develop bespoke solutions for clients, manage teams, and present findings to drive client satisfaction and retention.

Enterprise hunter role selling AI data solutions (data collection, annotation, LLM fine-tuning, generative AI) to Fortune 500 clients. Build pipeline, close high-value accounts, act as consultative SME on AI data preparation and workflows, and partner with solutions architects and PMs to ensure successful delivery while exceeding revenue targets.

The Scientist in Platform and TA Bioinformatics will design mRNA sequences, improve computational methods, handle biological datasets, and develop tools for sequence workflows.

Lead and scale an implementation engineering organization to productionize AI/ML systems (LLMs, multi-agent flows, RAG, hybrid architectures). Own architecture, CI/CD, observability, cost governance, and delivery practices; align engineering with product and business outcomes and mentor managers and senior engineers.

Design and execute test strategies and automation for cloud-native and GenAI/AI-ML systems. Perform functional, integration, performance, security, and API testing; validate model accuracy, fairness, robustness, and data quality; integrate automation into CI/CD; track defects; mentor junior QA engineers.

Conduct research in machine learning and AI, focusing on robotics and AGI, while collaborating with teams and reporting findings.

As a Staff Data Scientist, you will address complex business challenges using AI and optimization algorithms, collaborate with engineering, define strategies, and implement data product frameworks.

As a Machine Learning Engineer, you will design and deploy ML models, integrating LLMs with cultural intelligence and developing recommendation systems, while collaborating with data engineers and optimizing model performance.

Develop and implement state-of-the-art machine learning models and statistical methods using massive structured and alternative datasets to discover predictive signals, design experiments, validate ideas through simulation, and collaborate to move research into production trading strategies.

Own end-to-end AI-powered product improvements: design and build cross-cutting AI services (retrieval, evaluation, guardrails), define success metrics, prototype to production, create shared AI tooling and abstractions, shape data and system architecture for safe longitudinal signals, mentor engineers, and ship reliable, secure production systems that improve educator and family outcomes.