Top Tech Jobs & Startup Jobs in Los Angeles, CA

Own the AISim Data Platform roadmap across data ingestion, schema reconciliation, lineage, provenance, knowledge graphs, governance, and federated data sharing. Lead development of partner ingestion templates, matching experiences, experiment APIs, and compliance workflows. Define platform metrics and influence engineering, science, design, and trust teams to deliver scalable, secure data products for drug discovery and materials research.

Develop and execute joint marketing programs with strategic technology partners including AWS, Google Cloud, Microsoft, and OpenAI. Translate scientific research, customer outcomes, and AI capabilities into partner-facing content, enablement, campaigns, events, and marketplace assets. Generate and route partner-sourced leads, support account-based marketing, manage co-op and MDF funds, track pipeline and ROI, and report performance to marketing leadership.

Lead platform and infrastructure architecture for scalable AI workloads. Design resilient distributed systems, foundational platform services, and cloud environments; solve complex infrastructure challenges; mentor junior engineers; and collaborate with product, research, and security teams to improve reliability, performance, developer experience, and operational efficiency.

Lead a small AI engineering team while remaining hands-on in research and production software development. Build agentic capabilities for monitoring, guardrailing, evaluation, collaboration, and security. Perform exploratory data analysis, feature engineering, model fine-tuning, prototyping, and technical design. Translate AI research into scalable product features, set technical direction, mentor engineers, and deliver reliable production code.

Develop and optimize generative AI architectures for material and molecular discovery, including diffusion models, flow matching, and GFlowNets. Integrate reward signals from learned predictors and simulations, manage data ingestion and quality control, develop computational workflows, and deploy production-grade ML capabilities. Collaborate with ML researchers, computational scientists, engineering, product, and R&D teams. The role requires expertise in applied ML, scalable frameworks, cloud platforms, and material discovery, with preferred experience in active learning, Bayesian optimization, guided generation, and agentic or LLM methods.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Develop and deploy machine-learned interatomic potentials for PFAS substitution. Responsibilities include generating and curating first-principles training data, building reproducible cloud and HPC workflows, training and validating ML force fields, running molecular dynamics and quantum chemistry campaigns, predicting physical properties, extending models to condensed-phase and process chemistries, and communicating model uncertainty and domain limitations.

Designs and manages multi-cloud infrastructure across GCP, AWS, and Azure using Terraform and Kubernetes. Provides hands-on developer support, troubleshoots infrastructure and cluster issues, and maintains highly available systems. Responsibilities include networking, container orchestration, monitoring, logging, CI/CD automation, documentation, security collaboration, and on-call participation.

Own and negotiate complex commercial transactions, including customer agreements, research collaborations, partnerships, data licenses, SaaS contracts, vendor agreements, and professional services agreements. Partner with sales, science, finance, and R&D teams, particularly on life sciences and computational chemistry deals. Build scalable legal infrastructure through playbooks, templates, fallback positions, self-service processes, and AI-enabled contract workflows.

Own the catalysis product strategy, roadmap, and commercial expansion for an AI-driven materials discovery platform. Translate CHIPS Act objectives into product requirements and fulfillment evidence, define scientific datasets and model benchmarks, guide reaction-level modeling, and quantify customer value. Partner with scientists, program managers, business development teams, industrial customers, semiconductor organizations, catalyst manufacturers, and national laboratories. Lead segmentation, packaging, pricing, strategic partnerships, KPIs, and build-versus-buy decisions across refining, chemicals, emissions control, semiconductors, and energy-transition markets.