Top AI & Machine Learning Jobs

Product-oriented senior ML role to build, deploy, and operate models and ML systems (classification, extraction, entity resolution, clustering, ranking, anomaly detection, forecasting, LLM pipelines). Define datasets, labeling, evaluations, monitoring, and collaborate with engineering and product to ship production-quality training/inference pipelines and model-serving infrastructure.

Develops AI agents and language-model systems for automated code review and validation. Responsibilities include designing multi-agent architectures, building models, harnesses, and evaluations, researching LLMs and information retrieval, prototyping code-review workflows, and integrating successful experiments into production. The role requires strong programming, research ability, and product intuition, with opportunities to work on autonomous testing and deployment systems.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and end-to-end experiments. Select and adapt open-weight models, design robust rewards, deploy models to production, and improve behavior using real-world usage. The role requires strong Python engineering, experience taking models from raw data to production, GPU familiarity, and collaborative remote work.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and model release gates. Run end-to-end training experiments, select open-weight models, improve tool-using and multi-turn behavior, deploy models to production, and iterate using real-world usage. The role emphasizes Python engineering, reinforcement learning, GPU-based training, data quality, reward design, and production machine learning.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

PhD-level AI Engineering intern to apply machine learning to large-scale intraday and tick financial data. Responsibilities include alpha generation, portfolio optimization, risk modeling, data preprocessing, and collaboration with researchers to deploy robust ML models for trading.

Support an agency-wide AI initiative by analyzing AI usage, evaluating solution compatibility with enterprise infrastructure, and maintaining an enterprise AI landscape. Provide technical guidance on AI governance, risk, ethics, compliance, interoperability, scalability, security, and data governance. Participate in technical reviews, recommend improvements, and collaborate with program teams, stakeholders, and developers to align AI/ML initiatives with organizational goals and regulatory expectations.

Develop and deploy AI and deep learning models for antibody drug design, including test-time compute strategies, multi-objective optimization, target-specific design, and protein structure prediction. Analyze in silico and in vitro validation results, improve design methodologies, publish high-impact research, collaborate across computational and experimental teams, and communicate findings to interdisciplinary audiences.

Lead PayJoy’s ML platform organization and manage Staff and Senior ML and software engineers. Define the long-term roadmap for ML infrastructure, feature stores, model serving, ETL, internal APIs, deployment, monitoring, and MLOps. Partner with Risk, Fraud, Engineering, Product, and Data Science stakeholders to translate business goals into scalable technical solutions. Mentor engineering leaders, ensure production reliability and compliance, identify operational bottlenecks, and guide adoption of technologies such as LLMs and graph databases.

Leads customer-facing AI pre-sales engagements and designs tailored enterprise AI architectures. Advises executive stakeholders on AI strategy, ROI, governance, secure LLM integration, RAG, cloud deployment, networking, datacenter infrastructure, and conversational or agentic AI. Partners with sales, engineering, and technical domain teams to evolve AI offerings, guide digital transformation, and promote scalable, ethical, high-performance deployments.