Top AI & Machine Learning Jobs

Conduct applied research on user representation learning, transformers, and generative AI; build and deploy large-scale embedding, retrieval, and semantic search systems; collaborate with engineering and product teams to productionize models and run rigorous evaluations.

Build and improve production AI/data pipelines, design model evaluation workflows and Jupyter analyses, add observability to AI systems, experiment with models/retrieval/prompt architectures, integrate and abstract AI APIs, and help define inference serving, fine-tuning, and dataset-generation infrastructure.

Conduct applied research on user representation learning, multimodal and transformer-based generative AI. Design, train, evaluate, and deploy large-scale embedding, retrieval, and semantic search systems. Collaborate with engineering and product teams to build production-quality models, establish reproducible benchmarks, and translate research into measurable business impact.

Lead design, build, and validate end-to-end advanced AI systems (LLMs, agentic/multi-agent, distributed). Run hands-on evaluations, benchmarks, and prototypes; integrate emerging AI into cloud/hybrid/edge environments; ensure production rigor; provide technical leadership, architecture reviews, and convert prototypes into reusable frameworks and scalable solutions.

Conduct original ML research (LLMs, RL, multi-agent systems, diffusion/inverse models, trustworthy AI), publish and patent, build production-ready prototypes with GPU/HPC acceleration, collaborate across teams, and mentor junior researchers to translate research into deployable solutions for data center optimization, energy, and scientific computing.

Design and evaluate predictive and generative models, extract insights from disparate clinical and product data, build and partner on data pipelines, run rigorous evaluations and experiments, and deliver time-sensitive ad hoc analyses to product, engineering, and clinical stakeholders.

Lead and define AI research programmes to build novel architectures and evaluation methods for automated knowledge acquisition, reasoning, and large-scale journalism evaluation. Design experiments, implement and evaluate new ML methods, mentor research engineers, and publish results while delivering deployable, production-quality systems.

Design, implement, and evaluate novel AI systems (LLMs, automated reasoning, knowledge acquisition) for assessing journalism. Translate research ideas into scalable prototypes, run experiments, adapt/fine-tune models, develop new algorithms and evaluation methods, and communicate results via reports and papers.

Architect and lead AI-hardware co-design solutions from research to validated proofs-of-concept. Quantify compute trade-offs with high-fidelity simulation, optimize algorithms for hardware (memory, dataflow, precision), select compute substrates from MCUs to custom accelerators, mentor engineers, and translate innovation into deployable systems while driving thought leadership in Edge AI.

Define and own AI evaluation strategy for Figma's AI features: set quality dimensions and bars, build evaluation frameworks, golden datasets, and pipelines, combine human and automated/model-based evaluation, produce dashboards and readouts, partner with Product/Design/Engineering/Data Science, and manage a small team to ensure decision-ready signals for shipping AI features.

Lead and execute research on large language models and agentic systems, running large-scale post-training experiments (SFT, RLHF, RLAIF, DPO, GRPO), building evaluation benchmarks and data synthesis pipelines, translating research into model improvements and production deployments, publishing at top venues, and mentoring researchers and engineers.

The Staff Research Scientist will define research directions in AI and LLMs, develop models and systems for autonomous decision-making, and lead efforts to translate research into impactful solutions while mentoring team members.

Design, train, and evaluate learning algorithms connecting foundation models to physical systems for embodied agents. Focus on world models, action models, RL, and learning from demonstration in simulation and real-world deployments. Own end-to-end research projects, run large-scale experiments, collaborate with engineers and neuroscientists, and apply neuroscience principles to improve representations and behaviors.

As a Research Engineer, you will tackle AI safety challenges, develop evaluation methodologies, conduct adversarial testing, and work on model alignment and interpretability. You'll bridge theoretical and practical applications of AI safety research and collaborate with product teams to implement safety solutions.

Develop models and algorithms to enhance scientific research using AI agents. Collaborate with a multidisciplinary team and manage research projects end-to-end.

Design and build systems that automate and accelerate research workflows: evaluations, data and evaluation flywheels, agent harnesses, RL environments, experiment orchestration, sandboxed runtimes, integrations with research infrastructure, and metrics to measure research productivity and safety.

The AI Research Engineer will design, implement, and improve AI systems for therapy, enhance evaluation methods, and collaborate with various teams to innovate and ensure safety in AI therapy.

As an Applied AI Research Engineer, you'll work on ML projects, build models, integrate them into platforms, and drive business impact through AI applications.

Lead research on human-AI perception and interaction: propose and prototype ideas, publish and patent results, build interactive demos, collaborate with engineers and external researchers, mentor interns, and transition research into products.

Develop an AI-driven dual robotic system combining a vision-language planner with a fast reactive controller for precision fluid handling in radioisotope production. Build 3D perception for fluid-state estimation, develop reactive control methods, validate on a cold bench, collaborate across robotics, radiochemistry, and ML communities, and publish results in peer-reviewed venues.

12-16 week internship implementing and optimizing GPU/accelerator kernels and custom operators, profiling and improving LLM inference and runtime performance, building benchmarks, fixing regressions, and shipping code to production or open-source AI infra projects.

Design, implement, and train state-of-the-art 3D reconstruction and egocentric optical flow models from scratch. Scale multi-view architectures across multi-GPU clusters, innovate losses/architectures, prototype perception solutions, integrate neural rendering (NeRF, Gaussian Splatting), and produce motion-aware outputs for downstream robotics.

Design, train, and deploy large-scale AI/ML models for multimodal biological data; build research-grade ML infrastructure and data pipelines; collaborate with scientists and engineers; establish best practices and community standards; publish and open-source methods and code.

The AI Research Scientist will develop novel algorithms, lead experimental design, and mentor engineers while focusing on privacy-preserving AI solutions.

Conduct AI research to develop new approaches, algorithms, and methodologies for portfolio companies; influence technical direction, drive AI system improvements, and collaborate with founders to accelerate growth and impact.