Machine Learning Jobs in Berkeley, CA

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

Develop Cloud Data Infrastructure for Assured AI in autonomous vehicles, focusing on ML model training, evaluation, and deployment.

Develop deep learning models for autonomy applications, implement ML algorithms for self-driving vehicles, and resolve real-world testing issues.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

Design, build, and operate ML infrastructure powering data, compute, artifacts, and orchestration across cloud and on-prem. Own backend services, storage, observability, security, and developer tools; collaborate with cloud/compute providers and lead reliability and scaling efforts.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.

Lead development of a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.

Drive research and implementation of large-scale pretraining for protein generative models. Design autoregressive, diffusion, and representation-learning architectures, curate datasets, build pretraining infrastructure, evaluate models, and collaborate with computational and experimental teams to shape ML strategy for biomolecular design.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

As an AI/ML Platform Engineer, you will develop APIs, data pipelines, and workflows to support AI capabilities for scientific research, ensuring system reliability and scalability.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.