Top AI & Machine Learning Jobs

Build and ship AI products for enterprise clients as a senior individual contributor. Set technical direction, write production code, own end-to-end technical outcomes, and collaborate directly with CTO-level stakeholders. The role involves approximately 75% coding and 25% client engagement, applying rigorous software engineering practices to frontier AI systems and delivering multiple products across varied domains.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Own the research-engineering loop for turning base models into capable, reliable agents. Design RL environments, training and evaluation data, post-training experiments, reward systems, and regression suites. Analyze model behavior, diagnose reward hacking and other failures, improve distributed training and rollout infrastructure, and translate successful research into reproducible production pipelines. The role requires strong Python and software-engineering skills, modern language-model experience, deep-learning knowledge, and reinforcement-learning intuition.

Own applied data science for content performance, audience behavior, attribution, segmentation, and pilot performance prediction. Build models for engagement, sentiment, conversion, retention, and campaign attribution; track forecast accuracy and calibration; and translate findings into actionable recommendations. The role also mentors a junior analyst, strengthens statistical rigor, and partners with research and data science leadership.

Develop evolutionary algorithms for autonomous scientific discovery, enabling populations of models, hypotheses, programs, and representations to evolve through iterative search, selection, evaluation, and recombination. Build Bayesian and probabilistic methods for candidate ranking, uncertainty estimation, diversity preservation, and quality measurement. Establish benchmarks, integrate algorithms into autonomous-science pipelines, publish research, guide technical direction, and mentor colleagues as a senior individual contributor.

Participate in a one-time, remote guided English voice recording session (~20 minutes) using your own compatible device. Must follow recording instructions, complete session in a quiet indoor environment, be at least 18, reside and record from an approved U.S. state, and submit one participation only. Approved participants who meet quality standards receive $25.

Lead the architecture, development, deployment, and reliability of production AI systems. Establish technical standards, make architecture decisions, build observability and evaluation frameworks, mentor engineers, and translate client and product needs into scalable AI solutions. The role requires strong software engineering, full-stack or backend development, system design, technical leadership, and expertise with modern AI tooling.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.

Build and scale AI automations, agents, data pipelines, governance, spend dashboards, ROI metrics, and internal AI tooling. Manage Claude access, connector security reviews, AI champions, documentation, training, and executive reporting. Partner across departments to deploy production software, measure adoption and cost, and ensure teams own the tools they use.

Design and build a production AI agent platform with RAG pipelines, document parsing, structured data mapping, and multiple agent types. Deploy and operate ML models on GPU infrastructure while ensuring scalability, security, compliance, observability, and performance in a multi-tenant cloud environment. Collaborate with Product Management and engineering teams, provide technical leadership through design and code reviews, and mentor engineers.