Top AI & Machine Learning Jobs

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.

Designs and ships Python-first AI/ML platforms and generative AI applications (including agents). Builds production-grade, secure, scalable solutions using IaC (Terraform), CI/CD, and cloud. Partners with AI researchers and data scientists to move experiments to production, automates remediation, and applies responsible AI and enterprise-authorized AI-assisted development tools.

Conduct deep AI and quantitative research for electronic trading by pre-training Transformer and time-series foundation models on large-scale market data. Develop representations, objectives, architectures, distributed training methods, evaluation protocols, and model-serving components. Fine-tune models for alpha generation, pricing, market making, execution, and risk management while studying scaling laws, transferability, robustness, data efficiency, latency, and economically meaningful trading outcomes.

Lead the research, architecture, and production delivery of agentic and generative AI solutions for complex banking operations. Design collaborating agents, reusable services, evaluation frameworks, and distributed machine learning systems. Establish experimentation, monitoring, safety, reliability, and governance practices while partnering with business, product, and engineering teams. Mentor engineers and researchers and drive technical alignment across stakeholders.

Conducts research on algorithms for large language models and agentic AI systems, improving learning efficiency, inference performance, accuracy, latency, and compute cost. Develops theoretical foundations, implements performant software, validates methods at scale, solves problems for internal teams, publishes findings, presents research, and contributes to intellectual property protection.

Lead exploratory AI research and innovation initiatives, evaluating emerging techniques such as neuro-symbolic AI, causal AI, agentic AI, reasoning systems, and knowledge-enhanced machine learning. Design prototypes, assess capabilities and risks, identify business applications, and collaborate with engineering teams to transition successful concepts into production. Represent TransUnion through publications, patents, and presentations while shaping long-term AI strategy.

Conduct foundation-model research for robotic manipulation and high-precision manufacturing. Responsibilities include data curation, simulation, model training, evaluation, rigorous testing on industrial robots, and deployment to production hardware. The intern will work independently on ambiguous AI problems, develop multimodal models, use reinforcement or imitation learning, collaborate cross-functionally, and write maintainable production-quality R&D code.

Conduct foundational and applied ML research on AI well-being and moral status, design and run experiments (including tooling/infrastructure), collaborate with external researchers, and publish papers, blog posts, and talks to inform AI developers, policymakers, and the public.

Research Staff will develop foundational voice AI technologies, including low-bitrate neural audio codecs, steerable speech generation, disentangled audio representations, latent recombination, synthetic audio data generation, and multimodal speech-to-speech models. The role also involves designing scalable architectures, training methods, and inference algorithms optimized for hardware, billion-hour datasets, and real-time deployment. Candidates need strong mathematical foundations, foundation-model expertise, large-scale data pipeline experience, rigorous experimentation skills, deployment optimization knowledge, and publications or open-source contributions in speech or language AI.

Conduct applied AI research and build scalable production machine learning systems. Responsibilities include training, fine-tuning, evaluating, and deploying deep learning models; adapting current research; supporting distributed and accelerator-based training; implementing observability and safeguards; and improving systems continuously. The role spans problem framing, experimentation, productionization, and applied AI project delivery, with potential work involving large language models, retrieval-augmented generation, agentic systems, multimodal architectures, and responsible AI.

Conduct industry-focused AI research on autonomous agents and world models. Develop models involving large language models, active inference, and optimal policies, including architecture experimentation, fine-tuning, deep-learning agent development, and action optimization. Candidates will work on real-world enterprise problems while pursuing cutting-edge research during a fixed-term three- or six-month fellowship.

Conduct independent AI research for predictive toxicology and drug safety. Develop novel deep learning architectures, multimodal models, representations, uncertainty methods, and mechanistic reasoning approaches for heterogeneous biological and chemical data. Design rigorous experiments, benchmarks, ablations, and generalization evaluations while collaborating with engineering and scientific teams to translate validated research into scalable modeling systems. Contribute to scientific strategy, publications, collaborations, and regulatory validation.

Leads research and implementation of generative machine learning models, including causal graphs, planning algorithms, optimization, and training/inference pipelines. Builds reproducible ML infrastructure, integrates synthetic and real datasets, packages models into containerized services, and delivers production-quality research software. Presents findings to technical and government stakeholders, documents results, and collaborates with RF, DSP, formal methods, and academic research teams.

Conduct open-ended AI research focused on end-to-end training workflows using LanceDB. Design replicable experiments and benchmarks, demonstrate workflows across AI domains, develop content, publish research and models, and collaborate with engineering and product teams. The role requires deep learning expertise, experience training state-of-the-art models, open-source software development, strong prioritization, product awareness, and staying current with AI research.

Lead large-scale AI research programs end-to-end: define scope, align cross-functional teams, drive release planning, remove obstacles, report status and metrics, and partner with executives to deliver AI research into production at enterprise scale.

Research and develop machine learning systems, algorithms, prototypes, and data-fusion solutions for complex sponsor problems. Responsibilities include data selection and modeling, statistical analysis, anomaly detection, visualization, software integration, technical reporting, collaboration with sponsors, and supervision of junior staff. The role supports classified defense and intelligence applications and is performed onsite in State College, Pennsylvania, or Reston, Virginia.

Conduct AI and machine learning research focused on physics-driven modeling, simulation, reinforcement learning, multi-agent systems, and hybrid models. Develop and integrate novel algorithms into prototypes and production AI systems. Collaborate with research, engineering, and product teams, contribute to technical publications and patent disclosures, and support mission-critical modeling and decision-support solutions for space operations.

Identify enterprise AI opportunities, evaluate emerging models and platforms, build prototypes and proofs-of-concept, quantify business value, and present recommendations to technical and executive stakeholders to drive AI adoption across the company.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.

Conduct novel AI/ML research and develop algorithms, benchmarks, and production-quality code. Collaborate with product and engineering teams to deliver scalable solutions across banking businesses, publish findings, and provide thought leadership.

Lead research and production deployment of LLM-based agent systems for multi-step financial workflows. Define research directions (planning, tool use, safety), fine-tune and optimize models (PEFT, distillation, quantization), apply RL and preference optimization, scale systems (caching, batching, governance), implement privacy/safety controls (PCI, auditability), design experiments and metrics, and partner cross-functionally to deliver safe, explainable AI at scale.

Lead and conduct commercially oriented AI research: formulate problems, develop algorithms and benchmarks, run experiments, produce production-quality Python code, collaborate with engineering and business stakeholders, publish findings, and own strategic research portfolios that drive business impact.

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.