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Lead the design and deployment of auto-labeling systems and computer vision models to scale data pipelines for autonomous driving, using C++, Python, PyTorch, and TensorFlow.
Build and scale large vision-language foundation models for autonomous driving, using multimodal pre-training and reinforcement learning to improve scene understanding and autolabeling.
Develops and deploys machine learning models to improve autonomous driving behaviors, ensuring safety and performance at scale using C++ and Python.
Build and deploy multimodal LLMs and world models for 3D perception in autonomous vehicles using camera, LiDAR, and radar data.
Build and scale ML systems that continuously improve autonomous vehicle perception models using active learning, data curation, and large-scale pipelines.
Develops AI foundation models for autonomous driving, integrating large-scale systems with production platforms and adapting models to new sensors/platforms while collaborating across Alphabet teams.
Staff Tech Lead ML Engineer on Waymo's Perception team, designing multi-sensor model architectures for autonomous vehicle scene understanding and optimizing models for onboard compute using Python, C++, and frameworks like PyTorch/JAX/TensorFlow.
Senior ML engineer on Waymo's Special Vehicle Compliance team building multimodal perception for autonomous vehicles: training multi-task deep learning models (PyTorch/JAX) that fuse camera, LiDAR, and audio, developing high-resolution vision transformer backbones, and running large-scale data mining/auto-labeling pipelines, with model optimization for onboard accelerator inference.
Waymo is hiring a Sr Staff Tech Lead / ML Engineer for its Perception team to set the technical roadmap for next-generation multi-modal perception of the Waymo Driver - architecting scalable sensor-fusion models, optimizing them for onboard hardware, leading cross-functional initiatives, and mentoring engineers. Core stack: Python, C++, and modern ML frameworks like PyTorch, JAX, and TensorFlow.
ML Engineer (Machine Learning) Location: Remote (EST) or Onsite (Philly/DC/CA) 3 - 6 Months $45-$50/HR Tech: Python, PySpark, AWS, Databricks IV Process: 3 Rounds! Screen Coding System Design Role: Build custom DL…
Principal ML Engineer at HubSpot builds AI systems that extract context from CRM data to power customer-facing features using deep learning, retrieval, and NLP.
hackajob is collaborating with LexisNexis to connect them with exceptional professionals for this role. Are you looking to develop your Machine Learning Engineer career? Do you enjoy coaching others to achieve high…
hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role. JOB DESCRIPTION Are you looking for an exciting opportunity to solve exciting business problems? Our Technology…
hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role. JOB DESCRIPTION Build and deliver scalable, production-grade data and cloud solutions for regulatory model…
Machine Learning Engineer at a defense-tech company who owns the training, data, and edge-inference backbone for vision and multi-sensor autonomy models — building data/training pipelines, generating synthetic data, and deploying PyTorch models in real time on Jetson-class embedded hardware.
This role develops AI/ML models for satellite intelligence processing using Python, PyTorch, and TensorFlow. The engineer will work onsite in Beavercreek, OH on OPIR data exploitation for defense applications.
Trainee machine learning engineer at QBrainX in Coimbatore, India, working with data scientists and senior ML engineers to build, train, and deploy models that solve real-world problems. Core stack: Python with scikit-learn, pandas, NumPy, TensorFlow or PyTorch, plus Git.
AI/ML engineer at DataZymes in Bengaluru building and productionizing GenAI applications (RAG, multi-agent systems, Text2SQL, fine-tuning) for healthcare/pharma analytics clients. Core stack: HuggingFace, LangChain, DSPy, pandas, scikit-learn, PyTorch, and cloud ML platforms (AWS/Azure/GCP); Databricks/Spark is a plus.
Senior Machine Learning Engineer building scalable ML solutions for customer insights, marketing investment optimization, and data-driven decision-making. Core stack: Python, SQL, PySpark/Big Data, TensorFlow or PyTorch, and cloud ML tools like AWS SageMaker or Databricks. Hybrid in Mexico City or Guadalajara; remote from other Mexican states.
Senior machine learning engineer on Apella's Forecasting team, building and maintaining production ML pipelines for automated model retraining, deployment, and serving. Core stack includes Python, Docker/Kubernetes, CI/CD (GitHub Actions, ArgoCD), Terraform/Helm, and orchestrators like Dagster/Airflow in a healthcare (surgical) setting.
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