Top AI & Machine Learning Jobs

Design, prototype, and productionize agentic LLM systems: frame problems, build datasets, develop models/agents, evaluate and monitor performance, and deploy robust, observable production code while partnering with cross-functional teams.

The AI Researcher will conduct advanced research in Generative AI and Machine Learning, develop specialized models for observability and code repair, and collaborate with product teams to integrate findings into Datadog's offerings.

The AI Engineer will focus on building AI experiences, deploying models, and enhancing data workflows, working on greenfield applications. Key responsibilities include feature development, architecture collaboration, and shipping impactful product experiences.

Develop and deploy models to understand technical drawings and specifications, perform multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to deliver customer-impacting applied research.

Support research scientists with experiments involving advanced reasoning, computer use, and agentic AI models. Interns will collect, process, and analyze proprietary datasets; develop and test algorithms; document findings; present research; and collaborate with interdisciplinary teams. The role offers opportunities to contribute to publications and work with specialized datasets and computational resources. Interns work 10–40 hours weekly depending on company needs.

Develop computer vision and vision-language models for image and video understanding in restaurant environments. Build datasets, training strategies, evaluations, and benchmarks; diagnose model failures; and deploy efficient, reliable inference pipelines. The role combines research and engineering across scene understanding, object tracking, activity recognition, temporal reasoning, visual grounding, and multimodal models, while partnering with product and infrastructure teams to bring research into production.

Build reinforcement learning environments, evaluations, reward functions, and tool-using agents for chip-design tasks. Design and analyze experiments, train models, investigate failures, improve evaluation and research infrastructure, and turn successful approaches into reusable code and workflows. Collaborate with AI researchers, software engineers, and hardware specialists on RTL generation, verification, and physical-design optimization while ensuring correctness and measurable performance.

Research and develop AI solutions for complex industrial and energy-sector challenges using machine learning, deep learning, generative AI, and foundation models. Build scalable prototypes, collaborate with multidisciplinary researchers and business units, publish findings, create intellectual property through patents, and support grant proposals. Work may involve power generation, renewable energy, electric grids, robotics, and manufacturing.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Leads Mercury’s cross-line AI/ML research capability across insurance products. Responsibilities include setting strategy, building a research tooling team, modernizing workflows, developing model monitoring and AI infrastructure, establishing responsible AI guardrails, partnering across technology and data science teams, driving adoption, and coaching talent. The role requires strong quantitative, insurance analytics, Python, machine learning, generative AI, and LLM evaluation expertise, along with substantial people and cross-functional leadership experience.

Train large-scale deep learning models for AI-generated content detection, create synthetic datasets, conduct research in adversarial robustness and interpretability, and develop innovative model improvements. Publish research through technical papers and posts, ship production code, deploy models, build infrastructure, and collaborate with engineering and product teams to turn research into product features.

Develop AI/ML-enabled applications and agentic AI workflows for FAA mission capabilities. Responsibilities include conducting analysis and experimentation, building Python software, integrating LLMs and APIs, developing prototypes, contributing across data processing and backend systems, evaluating emerging AI tools, and collaborating with engineering teams to deliver practical mission-oriented products.

Conduct applied AI/ML and optimization research for modeling, simulation, and analysis applications. Develop surrogate models, software prototypes, and deployable analytics using Python, C/C++, and ML frameworks. Collaborate with research teams to translate customer needs into technical solutions, evaluate performance, and communicate findings through documentation, presentations, proposals, and publications. Maintain reproducible Linux-based development environments using version control, containers, and clean software practices.

Develop agentic AI systems for industrial automation, including workflow automation, code generation, debugging, root-cause analysis, optimization, and decision support. Build architectures combining LLMs, tool-using agents, retrieval, memory, reinforcement learning, and multimodal reasoning. Integrate AI with engineering tools, digital twins, operational data, and automation platforms while translating research concepts into deployable systems through collaboration with industrial domain experts.

Lead research and development of generative AI and recommendation solutions for Netflix promotional assets and content discovery. Responsibilities include tracking LLM research, developing summarization and LLM-as-judge systems, training and evaluating models, optimizing generated content for audience impact, partnering with engineering teams, defining ML roadmaps, and communicating technical strategies to stakeholders.

Conduct AI research for semiconductor manufacturing equipment, develop and evaluate solutions, translate research ideas into Python or C/C++ code, contribute to existing and new codebases, and share technical insights with the AI research team. Interns will complete meaningful project work, apply Lean principles, collaborate across cultures, and support the organization’s AI/ML research roadmap.

Leads and mentors a team of AI Research and Operations Analysts while scoping organization-wide AI requests, guiding GenAI experimentation, troubleshooting technical and operational issues, and partnering with product, engineering, and business stakeholders. Ensures prototypes, evaluations, automations, and documentation meet quality and business objectives while helping scale AI adoption across the organization.

Lead and conduct ML research aligned to Sprinter Health's strategy: define research agenda, design rigorous experiments, develop novel methods and evaluations, publish and patent findings, translate research into production-ready tools, and collaborate with clinicians and cross-functional teams to validate and deploy clinically robust AI solutions.

Lead the AI research agenda at Kepler, focusing on trustworthy AI for enterprise decisions. Oversee research on agentic systems and evaluation frameworks, and manage a research team. Ensure production deployment of innovative AI solutions based on real financial data.

Develop and deploy scalable, production-ready AI and machine learning models for protein, antibody, and broader drug discovery. Collaborate with AI scientists, engineers, and drug discovery experts to translate research concepts into robust systems, support large-scale model training, and deliver measurable impact in life sciences and healthcare.

Conduct AI and machine learning research through experiments, model evaluation, prototyping, quantitative analysis, and technical documentation. Investigate retrieval and knowledge systems, emerging models, and AI-enabled products while collaborating with researchers and engineers to translate findings into real systems.

Lead and conduct high-impact AI research across multi-agent systems, foundation models, continual learning, multimodal AI, synthetic data, and formal methods; translate research into production, collaborate with engineers and stakeholders, publish findings, and drive reusable assets and business solutions.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Conduct applied research on foundation models for real-time fraud detection using large-scale behavioral and sequential data. Design experiments, benchmarks, holdouts, and monitoring systems; develop models from data preparation through deployment; and optimize training and inference efficiency. Collaborate with engineering, client-facing teams, legal, compliance, and customers to productionize models and establish explainability, governance, and risk documentation.

Build and evaluate AI models and products, develop demos, prototypes, and technical evaluations, and support model launches. Partner with Product, Research, Sales, and Applied AI teams to enable field readiness, solve strategic customer challenges, and translate customer insights into product and research improvements. Create playbooks, training, and reference architectures while serving as a technical expert on emerging AI capabilities. Occasional customer-site travel is required.