Top AI Research Jobs in Los Angeles, CA

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

The Research Engineer will work on neuroimaging projects, focusing on data processing, machine learning, and analysis of neurodegenerative disorders, while supervising students and contributing to scientific publications.

Lead development of a production-grade evaluation framework for planner-executor agents. Build user simulation, isolated experiment execution, grading, statistical analysis, tracing, optimization-lever plugins, and a locally hosted dashboard. Run experiments comparing models and inference strategies, quantify quality, latency, and cost tradeoffs, generate evidence-backed insights, and deliver client recommendations. Partner with engineers and business stakeholders, communicate findings, establish evaluation standards, and mentor others.

Evaluate conversations between users and an AI shopping assistant, identify logical errors, inaccuracies, and poor recommendations, and create structured rubrics and verifiers for judging response quality. This remote engagement requires at least 20 hours per week and focuses on improving model performance and e-commerce response quality.

Research and develop AI models for music generation, transformation, remixing, and creative interaction. Investigate generative audio and music understanding, design evaluation frameworks, and translate research prototypes into product capabilities. Collaborate with engineers, music technologists, producers, and musicians while addressing audio quality, musicality, controllability, music rights, artist consent, data provenance, and responsible AI practices.

Lead a research engineering team developing synthetic data pipelines, AI benchmarks, evaluation harnesses, and reinforcement learning environments. Partner with research scientists and frontier AI labs to design rigorous experiments, validate data quality, measure model impact, and publish findings. Responsibilities include hiring and coaching engineers, setting technical standards, building scalable Python-based systems, reviewing code, directing research agendas, and determining whether model improvements are statistically meaningful.

Lead frontier AI safety research focused on CBRNE and cybersecurity risks. Responsibilities include managing red-team datasets, evaluation protocols, technical risk reports, QA reviews, expert teams, client engagements, threat modeling, and architecture-level mitigation recommendations across text, vision, and audio systems.

Evaluate and improve coding agents by assigning real software engineering tasks, reviewing model-generated work, completing real coding problems, and identifying model failures. The role is a fixed-pay contract offering $6,000 per week for 40 hours, with flexible scheduling and ongoing weekly work. Candidates need at least four years of professional software engineering experience, a computer science or related degree, U.S. work authorization, strong written English, and availability for 40 hours weekly.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Explains how to locate people on social media using reverse image and facial-recognition search, highlights limits of Google Images and TinEye, and recommends Face2social for matching faces across platforms, even from old photos.