Top AI & Machine Learning Jobs

Data Scientist Intern supporting Syngenta’s Vegetables R&D team by processing large structured and unstructured datasets, developing and validating machine learning models for product placement predictions, integrating varied data types, and documenting findings and recommendations. The role involves collaborating with data scientists on research initiatives and applying AI/ML methods to genetic, phenotypic trial, and environmental agriculture data.

Develops computer vision and machine learning perception algorithms for autonomous space-threat detection. Responsibilities include training, evaluating, optimizing, and deploying models; predicting relative pose of dynamic targets; integrating perception into simulations and real-time autonomy pipelines; creating sensor models and digital twins; and maintaining model-training and evaluation infrastructure.

Build and scale enterprise AI tooling for software development, including agent runtimes, evaluation, guardrails, monitoring, and model-portable workflows. Lead AI service and developer-tool design from concept through production while ensuring reliability, security, privacy, auditability, performance, and cost efficiency. Collaborate with engineering, DevEx, IT, Security, Compliance, and Audit teams; measure developer impact; influence technical direction; and mentor engineers across levels.

Leads the US agentic AI consulting business, advising C-level clients, shaping AI ecosystems, developing ROI cases, overseeing delivery and governance, winning multimillion-dollar programs, and building a high-performing AI consulting team. The role combines strategic advisory, solution architecture, portfolio oversight, responsible AI governance, thought leadership, and commercial growth across marketing, CX, and technology engagements.

Develop advanced Bayesian models and stochastic simulations for business modeling, product innovation, user behavior, and system performance. Manage analytical projects end to end, including data preparation, model development, validation, deployment, and proof-of-concept applications. Communicate methodologies, progress, and findings through reports and stakeholder presentations.

Conduct applied machine learning research focused on language and multimodal models. Develop and evaluate novel methods for language understanding, generation, reasoning, adaptation, and continual learning. Build and optimize LLM and VLM systems using Python and deep learning frameworks, including distributed training. Communicate quantitative research findings to technical audiences and contribute through publications, patents, open-source work, or deployed systems.

Leads AI safety technical strategies and customer projects focused on safely integrating AI into software development and software systems. Responsibilities include project planning, resource coordination, risk and milestone management, quality assurance, customer liaison activities, technical requirements translation, executive reporting, strategic planning, and representing the AI safety portfolio. The role requires collaboration with researchers, engineers, customers, and support teams, plus the ability to obtain and maintain a Department of War security clearance.

Lead customer-facing technical programs for frontier AI labs, including reinforcement learning environments, training tasks, evaluation infrastructure, automated rewards, and verification systems. Manage multiple programs, high-volume task production, external technical contributors, cybersecurity and agent safety initiatives, and cross-functional delivery across research, engineering, product, sales, and customers.

Develop and maintain scalable backend services and APIs while integrating Generative AI models into enterprise applications. Responsibilities include fine-tuning LLMs, building microservices, implementing RAG pipelines, managing CI/CD, Docker, Kubernetes, and cloud deployments, and improving system reliability. The role also involves testing, code reviews, performance monitoring, and collaboration across engineering teams.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.