Top AI & Machine Learning Jobs

Build and deploy AI systems for startup customers, from initial implementation through ongoing adoption. Identify high-impact AI use cases, develop LLM applications and integrations, collaborate with customer engineering teams, and translate field insights into product roadmap improvements. The role requires strong Python or TypeScript skills, production AI experience, customer-facing communication, autonomy, and comfort working in ambiguous environments.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Build and operate secure, scalable AI/ML platforms across hybrid and on-premises environments. Develop data pipelines, MLOps workflows, Kubernetes deployments, CI/CD and GitOps automation, observability, infrastructure-as-code, and secure container supply chains. Support model serving, generative AI, and retrieval-augmented generation while troubleshooting distributed systems, improving reliability, and contributing to architecture, standards, documentation, incident response, and technical mentorship.

Lead and build Pfizer's AI-first design system across human and agent surfaces. Ship design standards-as-code, own conversational and agentic UX, drive adoption metrics, produce high-fidelity prototypes, advise executives, partner with AI/Product/Engineering, and grow a high-performing design team focused on safety, compliance, accessibility, and measurable experience outcomes.

Leads a small engineering team building customer-facing AI solutions and scalable cloud services in a regulated, high-scale environment. Remains hands-on with technical design, implementation, code reviews, troubleshooting, AI agents, MCP tools, integrations, and distributed systems using C#/.NET and AWS. Provides technical direction and mentorship while partnering with product, business, platform, and external stakeholders to deliver secure, reliable, observable production systems.

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

Leads adoption of AI-assisted software development and delivery practices across engineering teams. Partners with Engineering, SQA, Product, Architecture, and DevOps to improve SDLC workflows, automation, quality, delivery predictability, and engineering productivity. Establishes AI-enabled practices involving LLMs, intelligent automation, agentic workflows, prompt engineering, and guardrails while tracking metrics such as cycle time, rework, automation coverage, and AI adoption.

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.