Top AI & Machine Learning Jobs

Conduct an end-to-end machine learning research project focused on audio and voice AI. Responsibilities include literature review, implementation, experimentation, ablation design, model training and evaluation on large-scale telephony audio, presenting findings, and potentially moving successful results into production. Potential areas include text-to-speech, speech recognition, neural audio codecs, streaming inference, and conversational turn-taking.

Lead and develop a team of Applied AI Engineers while remaining hands-on in production code. Own technical outcomes, architecture decisions, engineering standards, customer technical relationships, hiring, and delivery across ambiguous enterprise environments. Partner with engagement and growth leaders, provide deliberate coaching and feedback, support customer deployments, and translate field insights into improvements for Mosaic, the company’s AI deployment toolkit.

Develop advanced analytics, data models, algorithms, and insights from large-scale geospatial datasets. Use Snowflake and SQL to build efficient data transformations and production data products, while applying Python, statistical analysis, machine learning, and visualization techniques. Identify data quality issues, optimize analytical processes, and independently drive projects from problem definition through delivery. Collaborate with development teams and communicate complex findings as actionable recommendations.

Develop reinforcement learning capabilities for autonomous marine vessels by building simulation environments, training pipelines, evaluation frameworks, reward functions, and experimentation workflows. Research RL algorithms, robotics frameworks, and simulation tools; conduct literature reviews and benchmarking; and contribute reusable development frameworks and best practices. The intern will collaborate with engineers on real-world marine autonomy projects while working 40 hours weekly from Spring through Fall 2027.

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.

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.