Top AI & Machine Learning Jobs

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

Lead product data science and analytics for Remitly’s high-value sender segment. Analyze customer journeys, funnels, and behavioral trends; design and evaluate experiments; apply statistical and causal inference methods; connect marketing activity to product outcomes; improve instrumentation and data quality; and influence product roadmaps through evidence-based recommendations. Mentor analysts, strengthen experimentation standards, and communicate insights and uncertainty to technical and business stakeholders.

Lead development and optimization of AI/ML algorithm solutions for computer vision, 3D modeling, multimodal sensor fusion, and imagery recognition. Implement and evaluate prototypes, mature them into operational capabilities, and support deployment across cloud and edge environments. Collaborate with researchers, engineers, and academic partners on national security applications, while leading research initiatives and communicating technical results to varied audiences.

Lead AI risk analyst responsible for assessing risks across safety, privacy, security, fairness, transparency, oversight, and compliance. The role supports AI governance reviews, vendor assessments, regulatory and certification activities, and continuous improvement of governance frameworks and processes. It also develops policies, guidance, training, and practical recommendations while collaborating with Product, Engineering, Security, Privacy, Legal, Procurement, and external stakeholders.

Analyze and optimize AI training and inference workloads across distributed systems. Design benchmarks, profile performance, identify bottlenecks, and optimize communication software using MPI, NCCL, UCX, libfabric, and RDMA. Translate modeling and benchmarking results into validated system improvements while coordinating technical work across hardware, software, networking, and architecture teams.

Ten-week machine learning engineering internship focused on building systems for training and deploying large-scale ML models. Interns collaborate with researchers, hardware experts, and software engineers on GPU acceleration, distributed computing, and open-source tools supporting trading strategies. The program includes technical training, mentorship, professional development, financial markets education, and team activities. Successful interns may receive an opportunity for a graduate position.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Lead Americas field engineering and technical delivery for an agentic AI platform serving insurers. Build agentic systems, harnesses, workflows, and internal tooling while personally supporting major pursuits. Grow and manage a player-coach team, improve delivery productivity, accelerate customer implementations, and translate field-built capabilities into the core product. The role combines hands-on AI engineering, enterprise go-to-market execution, customer transformation, and leadership.

Drives battery manufacturing improvements in yield, scrap, quality, and throughput using AI and analytics. Builds and deploys models for coating and formation quality, defect detection, and root-cause analysis. Converts model insights into process changes, error-proofing, and standard work while integrating MES, historian, PLC, OT, and metrology data. Collaborates with operators, technicians, process engineers, controls teams, quality teams, vendors, and central technologists to scale reusable solutions across plants.

Designs, develops, tests, and optimizes AI-powered workflows for legal practice groups. Creates prompts, workflow templates, knowledge resources, evaluation methods, and quality standards; translates attorney needs into testable AI solutions; monitors performance metrics and user feedback; supports pilots, training, adoption, vendor collaboration, and roadmap development. The role requires strong generative AI, prompt engineering, workflow analysis, quality assurance, and stakeholder communication skills in a legal technology environment.