Top AI & Machine Learning Jobs

Join a research and advisory pipeline for an asset-management startup building an auditable reasoning engine. Advise on agentic AI, world models, interpretability, and systemic risk. Help shape research direction, identify failure modes and guardrails, and provide scholarly evidence (papers, GitHub, Google Scholar) and a short written response to demonstrate expertise.

Modernize SQL Server systems by refactoring stored procedures, optimizing queries, designing migrations, and implementing event-driven patterns. Build AI-ready data infrastructure, including embedding pipelines, vector search, RAG retrieval services, synchronization workflows, and evaluation monitoring. Develop ETL/ELT pipelines across relational, cloud, and NoSQL platforms, supported by infrastructure-as-code, data quality controls, testing, and observability. Collaborate with engineering teams, troubleshoot production issues, document solutions, mentor engineers, and use AI coding assistants to accelerate modernization and development.

Design, deploy, operate, and support secure AI tools, gateways, integrations, and automated workflows. Manage model access, APIs, permissions, monitoring, audit records, testing, troubleshooting, releases, and documentation. Collaborate with engineering, cybersecurity, platform, product, and operations teams to improve AI assistants, prompts, and workflow reliability. Contribute to cyber operations, technical planning, code reviews, and agile delivery while mentoring junior team members.

Support the deployment, operation, integration, and troubleshooting of AI tools, gateways, and automated workflows. Build secure API and enterprise-system connections, develop scripts and documentation, manage access and logging, monitor operational health, assist with cyber operations, and improve AI prompts and configurations. Collaborate with engineering, cybersecurity, product, and platform teams in an agile environment while communicating technical risks, issues, and recommendations.

Designs, validates, deploys, and maintains supervised and unsupervised machine learning models and MLOps pipelines in AWS GovCloud and Databricks. Builds PySpark and Delta Lake data pipelines, feature stores, monitoring frameworks, and model governance documentation. Ensures compliance with federal AI safety and NIST requirements, supports ATO activities, produces performance reports, and briefs senior government stakeholders on model risk and roadmaps.

Productionize detection, tracking, and vision-language models across edge devices and cloud infrastructure. Build evaluation systems, data feedback loops, deployment pipelines, and production metrics; optimize inference cost and performance; run staged rollouts and experiments; and ship customer-facing features connecting inference services, APIs, and product behavior.

Build and operationalize insurance analytics, dashboards, visualizations, and data science solutions for Property & Casualty business lines. Collect, cleanse, and integrate insurance data; conduct exploratory analysis; develop statistical and database-driven solutions; prototype innovations; and communicate actionable insights to stakeholders. Collaborate with solution engineers and business partners to deploy analytics on Azure Databricks.

Leads analytics and AI enablement for connected vehicle services, translating business needs into reusable data products, advanced analytics, dashboards, and self-service capabilities. Develops scalable Python, SQL, Spark, and Databricks workflows; ensures data quality and governance; and supports AI assistants, agents, RAG systems, and machine learning use cases. Partners with technical and business stakeholders to improve customer understanding, product performance, and operational efficiency.

Leads the data science roadmap and manages Data Scientists and Analysts while remaining hands-on. Designs data pipelines, ETL/ELT processes, analytical datasets, machine learning models, predictive analytics, optimization, and AI solutions. Partners with executives and cross-functional teams to translate business needs into scalable solutions, establish success metrics, improve data governance, and deliver measurable operational and financial value. Coaches team members, develops organizational capabilities, and communicates analytical outcomes to technical and non-technical stakeholders.

Develops cost transparency, dashboards, forecasting, budgeting, governance, and optimization strategies for AI and machine learning workloads. Partners with engineering, data science, finance, procurement, security, and risk teams to improve cloud and vendor spend, GPU utilization, model deployment efficiency, and responsible AI adoption. Automates cost management using scripting, infrastructure-as-code, and cloud-native tooling while evaluating hosted, internal, and open-source AI platforms.

Supports data science projects by preparing and validating datasets, maintaining metadata, conducting exploratory analysis, developing and validating analytical models, researching data science solutions, and visualizing model insights. Works with team members to provide analysis and recommendations for business decision-making. The role requires a bachelor’s degree in data science, statistics, operations research, computer science, or an equivalent field, and typically targets candidates with 0–2 years of experience.

Leads enterprise strategy, portfolio governance, investment planning, and delivery roadmaps for data products, platforms, artificial intelligence, machine learning, and automation. Oversees responsible AI, platform modernization, data governance, vendor management, budgets, adoption, risk, and measurable business outcomes. Builds and leads multidisciplinary teams through Directors, Senior Managers, and Managers while partnering with business, product, engineering, security, legal, privacy, finance, and operations leaders.