Top AI & Machine Learning Jobs

Leads the design and development of data engineering and AI-enabled solutions for legal, client, and internal business applications. Builds Python and SQL workflows for structured and unstructured data, integrates generative AI and APIs, validates AI outputs, establishes human-in-the-loop quality controls, and supports responsible AI governance. Collaborates with attorneys and technical teams, evaluates emerging technologies, prototypes self-service tools, and provides technical guidance to less experienced professionals.

Support the design, development, testing, deployment, and optimization of AI and machine learning solutions for legal technology applications. Work with senior engineers on model evaluation, fine-tuning, retrieval and reasoning improvements, troubleshooting, MLOps, documentation, and production-grade enterprise AI systems.

Develop and deploy scalable, production-ready AI and machine learning models for protein, antibody, and broader drug discovery. Collaborate with AI scientists, engineers, and drug discovery experts to translate research concepts into robust systems, support large-scale model training, and deliver measurable impact in life sciences and healthcare.

Design and own Quince’s agentic infrastructure for marketing execution and user growth. Build scalable LLM services, multi-agent event streams, automated content and multimodal asset pipelines, and autonomous campaign optimization systems. Establish safe LLM operating protocols, including confidence limits, prompt versioning, fallback, and rollback frameworks. Collaborate with Product, Data Science, and Engineering teams to improve catalog data integration, reduce latency, and increase advertising delivery efficiency.

Develop and validate statistical and machine learning models that solve business challenges across R&D, manufacturing, finance, supply chain, and commercial functions. Translate complex data into actionable insights, optimize processes, support drug discovery and clinical analysis, improve forecasting and quality control, and communicate findings through data storytelling. Collaborate with engineering teams on deployment, promote data-driven decision-making, and mentor colleagues.

Build and refine AI-powered automation solutions for business workflows. Partner with stakeholders to identify automation opportunities, apply generative AI and conversational agents, test solutions against real-world data, document architectures, communicate results, and support deployment. Interns gain hands-on experience with Microsoft AI and automation technologies while researching emerging tools and measuring operational impact.

Develop, deploy, and maintain AI and machine learning solutions for municipal operations. Responsibilities include building ML pipelines and NLP models, preparing data, feature engineering, model evaluation, production monitoring, troubleshooting, documentation, and staff training. The role also supports responsible AI, data governance, enterprise system integration, and communication with nontechnical stakeholders.

Conduct original interpretability research with customers, analyze advanced AI models, prototype techniques to visualize and manipulate model structures, and translate research into production-ready tools. Lead high-stakes project delivery by defining hypotheses, sourcing data, building tools, and iterating with engineering and customer partners. Help shape the company’s research direction while communicating complex technical concepts clearly.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.