Top AI & Machine Learning Jobs

Conduct cutting-edge machine learning research involving frontier models, reinforcement learning, post-training, scalable oversight, synthetic data, safety, evaluation, and alignment. Interns develop and improve production ML systems, research new methods, conduct experiments, and may publish or ship work with mentorship from researchers and collaboration across industry and academia.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Design, train, and deploy deep-learning models that predict market prices; build tooling and large-scale ML systems to enable firm-wide ML adoption; simulate trading decisions and integrate model predictions into production trading systems.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Lead end-to-end product for voice protection and conversational AI features serving consumers and telecom operator partners. Define product vision, prioritize roadmap, work closely with engineering and research on model training and prototyping, engage customers to validate needs, support commercial and legal partners, and drive product economics and successful launches.

As an AI Engineer, you will architect AI systems for legal work, lead technical initiatives, and mentor a team while impacting the future of legal technology.

Lead development, deployment, and monitoring of underwriting, risk, fraud, and payments ML models. Own full modeling lifecycle, feature engineering, validation, A/B testing, cross-functional collaboration, and mentor junior data scientists.