Top AI & Machine Learning Jobs

Build and integrate ROS/ROS2 modules, collect and maintain ML training/test datasets from robots and mobile apps, implement end-to-end ML workflows, analyze data from internal databases, monitor data/model quality in production, and write maintainable Python/C++ code while collaborating across ML, engineering, product, and research teams.

Lead Kymera's enterprise AI strategy and roadmap; prototype, build, and deploy AI/LLM solutions; select and govern AI platforms; establish AI governance with InfoSec; manage vendors and external engagements; drive adoption through training; and advise functional leaders to operationalize AI across research, development, regulatory, and G&A.

Deliver end-to-end data science projects using machine learning and deep learning, collaborate with stakeholders, and support client engagement through analytical planning and solution design.

As a Data Scientist, you will collaborate with scientists to develop algorithms and software solutions for data analysis and machine learning, while engaging in multidisciplinary teamwork to meet project objectives.

Build production decision systems that estimate causal effects and uncertainty, learn from interventions, optimize constrained policies (pricing, inventory, promotions), evaluate counterfactuals and challengers, and deploy safe, automated ML-driven business decisions to generate measurable economic lift.

Six-month, on-site AI/ML co-op to develop, deploy, and monitor production ML and time-series forecasting models. Responsibilities include data sourcing/cleaning, building ML pipelines, model evaluation and validation, drift detection, retraining triggers, and maintaining audit-ready production models using ML tooling.

Perform data extraction (including VPN data), integrate into databases, and analyze datasets using statistical and computational methods. Apply knowledge of DX, XKS, and CASNs to guide data selection, build analytic models, and produce actionable insights. Translate mission needs into technical requirements and communicate results to non-technical stakeholders while accounting for government data constraints.

Build and maintain scalable AWS infrastructure, Terraform IaC, CI/CD pipelines, and MLOps platforms to support model training, deployment, monitoring, and lifecycle management. Partner with ML engineers to enable Generative AI and LLM applications (including AWS Bedrock), improve automation, observability, reliability, security, and cost efficiency across development and production environments.

Technical lead for data acquisition, analytics, governance, and Responsible AI. Design, develop, and deploy predictive/prescriptive/descriptive/cognitive AI/ML models (NLP, network and bibliometric analysis). Implement MLOps (model registry, drift monitoring, automated retraining), enforce RLS/CLS and federal data security frameworks, and provide training and stakeholder-facing dashboards to inform workforce, logistics, and operational decisions.

Design, train, evaluate, deploy, and monitor machine learning models. Prepare and analyze large datasets, improve model performance through experiments, and collaborate with software engineers to integrate ML solutions into production while staying current with ML techniques.