Top AI & Machine Learning Jobs

Design and develop MLOps platforms, model-serving services, training and simulation tools, data pipelines, and monitoring systems. Collaborate with data scientists and application teams to integrate machine learning models into business processes. Analyze large structured and unstructured datasets, develop automated decision-making algorithms, participate in code reviews, and ensure the quality and performance of machine learning applications.

Performs exploratory data analysis, feature engineering, predictive modeling, statistical testing, and data visualization on large structured and unstructured datasets. Develops, validates, and improves predictive and prescriptive models, identifies business opportunities, and presents findings to stakeholders. Collaborates with machine learning engineers, product, and engineering teams on model deployment and internal data platforms. Establishes modeling best practices, supports data collection, and mentors associate data scientists.

Leads and develops a team building AI enablement tools, developer tooling, reusable accelerators, automation, and shared engineering systems. Remains hands-on with coding, code review, prototyping, and AI-assisted development. Drives production adoption of AI through evaluation, governance, observability, resilience, and operational best practices. Establishes ownership in ambiguous environments and influences architecture, strategy, and infrastructure investment across engineering, security, product, and leadership teams.

Performs exploratory data analysis, feature engineering, predictive and prescriptive modeling, statistical testing, visualization, and business problem solving. Develops, validates, and deploys models in partnership with engineering and product teams, improves data sources and internal data platforms, communicates findings to stakeholders, and mentors associate data scientists.

Leads and mentors an engineering team building AI enablement platforms, developer tooling, automation, reusable accelerators, and shared engineering systems. The role remains hands-on with coding, code review, prototyping, architecture, production AI adoption, evaluation, governance, observability, resilience, and operational improvement. It partners across engineering, security, product, and leadership to define ownership, strategy, infrastructure investment, and measurable adoption outcomes.

Leads and develops an engineering team responsible for AI enablement, developer tooling, reusable accelerators, automation, and shared engineering systems. Drives adoption of production AI capabilities, improves observability and resilience, reduces operational toil, and influences architecture and investment decisions. Partners with engineering, security, product, and leadership to establish ownership, clarify ambiguous problems, and deliver measurable outcomes.

Designs and deploys AI/ML-powered image analysis software for high-content microscopy and translational research. Develops deep learning algorithms for segmentation, feature extraction, classification, and phenotypic analysis; builds scalable imaging pipelines; ensures software quality, reproducibility, metadata integrity, and FAIR data practices. Collaborates with scientists and technical teams, evaluates emerging technologies, mentors junior engineers, and contributes to scientific publications and research communications.

Build and scale agentic AI applications end-to-end: design, implement, deploy, and iterate full-stack features. Implement agent orchestration, tool calling, RAG workflows, observability, and guardrails. Collaborate with product, design, data, and distributed engineering teams; contribute to architecture, mentor engineers, and participate in on-call rotation to support production reliability.

Lead a hands-on engineering team building agentic AI applications (agent orchestration, tool calling, RAG, LLM features). Own architecture, technical strategy, evaluation, observability, and AI safety guardrails. Hire, coach, and grow engineers while partnering with product, design, data, and business stakeholders to deliver measurable product outcomes.

Build and ship full-stack, production agentic AI features that reason, plan, call tools, and act to drive business outcomes. Implement backend/frontend systems, agent orchestration, RAG workflows, observability/guardrails, and collaborate with product, data, and distributed engineering teams to iterate on measurable AI-native features.