Top Remote Machine Learning Jobs in Santa Cruz, CA

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Design and train foundation vision-language models that read entire CT studies end-to-end. Build custom PyTorch architectures and distributed training pipelines, run large-scale experiments, evaluate models for clinical safety, and move models through FDA submission to hospital deployment.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Lead architecture and maintenance of end-to-end traffic classification ML systems for adversarial domains. Own model lifecycle from offline evaluation to millisecond-edge deployment, build certified offline-to-online data pipelines, establish rigorous evaluation and leakage-prevention, optimize inference under strict latency/cost budgets, integrate scoring with mitigation workflows, and provide cross-functional leadership and mentorship.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

Lead inference acceleration and GPU-parallelism optimizations (TP, SP, PP); implement high-performance CUDA/NCCL kernels; deploy videogen and LLM models to production; collaborate with researchers, conduct code reviews, mentor engineers, and optionally improve training efficiency and resource utilization.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Lead technical vision and roadmap for a company-wide ML platform: define reference architectures, MLOps best practices, security, observability, and integrations; mentor engineers and drive cross-team platform initiatives.

Design and lead end-to-end applied AI architectures (predictive ML, MLOps, generative AI, LLMs, RAG, agentic workflows), translate business needs into production-ready solutions, prototype and deliver implementations, and ensure security, observability, governance, and operational reliability.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

As an AI/ML Presales Solutions Architect, you will engage with clients to identify problems, create feasible AI solutions, and partner with sales to ensure project delivery aligns with capabilities. You will leverage your extensive experience in AI and cloud platforms to shape presentations and technical discussions with clients.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

Optimize and deploy multi-modal models (transformers, diffusion, VLMs) to run on-device across mobile and desktop. Own export, quantization, kernel tuning, runtime integration, and profiling to meet strict latency, memory, and power budgets. Build packaging, benchmarking, and telemetry; partner with research to productionize models and mentor junior engineers.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.