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Lead a team of 3-4 engineers to build and scale ML infrastructure, focusing on orchestration, distributed training, and real-time serving across multi-cloud environments using MLOps best practices.
Machine learning engineer at Artera, a cancer-care AI company, developing and validating AI biomarkers and pathology foundation models from whole-slide images, clinical, and molecular data. Day-to-day work spans prototyping, robustness and interpretability work, reproducible pipelines, and regulatory-supporting validation, primarily in Python and PyTorch.
Software engineer on Wayve's AI Libraries team, building scalable Python libraries and training infrastructure (data loading, distributed training, checkpointing, evaluation) that let ML researchers train and scale autonomous-driving models across GPU clusters and Azure cloud.
Architects and operates the cloud, big-data, and ML platform for Inceptio's overseas autonomous-driving program in Singapore, building petabyte-scale ingestion and pipelines for camera/LiDAR/radar fleet data plus MLOps (GPU training, orchestration, CI/CD). Core stack: AWS/GCP/Azure, Spark/Flink/Kafka, Kubernetes, Terraform, Python and Go/Java/Scala.
ML Systems Engineer at Final, an HFT/trading-algorithms firm in Ramat Hasharon, building and optimizing proprietary deep learning systems for live trading. Day-to-day involves running DL models on large GPU clusters and adapting them for production serving, using PyTorch, Python/C/C++, and custom CUDA/Triton kernels.
Designs and maintains high-performance networks for AI cloud data centers, focusing on InfiniBand/RoCEv2 fabrics, NVIDIA switches, and automation tools to support large-scale GPU clusters.
Build and run the distributed GPU infrastructure that trains and serves PhysicsX’s large AI models for engineering simulation, partnering with research scientists to optimize training pipelines and model deployment.
You will provide technical leadership across ML lifecycle, including defining architecture, engineering standards, MLOps practices, and governance frameworks for a large-scale matching platform Defining end-to-end ML…
THE AI ACCELERATOR Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right…
Capgemini is hiring an MLOps Engineer in Bangalore to build and maintain end-to-end ML pipelines — training, evaluation, deployment, CI/CD for ML workflows, and model serving with TensorFlow Serving/TorchServe — while optimizing ML infrastructure on AWS. Core stack: Python, Docker, Kubernetes, and monitoring tools like Prometheus and Grafana.
ML Engineer in Boehringer Ingelheim's London-based AI Accelerator, turning validated biomedical AI research prototypes into production-ready foundation models (training code, data loaders, inference, optimisation like quantisation and distillation) that support drug-discovery and portfolio decisions. Core stack: Python, PyTorch, distributed training. Hybrid, ~3 days/week in the office.
Requisition Number: 30527 Required Travel: 0 - 10% Employment Type: Full Time/Salaried/Exempt Anticipated Salary Range: $61,880.00 - $85,000.00 Security Clearance: Ability to Obtain Level of Experience: Mid This…
Requisition Number: 26897 Required Travel: 0 - 10% Employment Type: Full Time/Salaried/Exempt Anticipated Salary Range: $75,866.00 - $90,001.60 Security Clearance: Secret Level of Experience: Mid This opportunity…
Requisition Number: 30472 Required Travel: 0 - 10% Employment Type: Full Time/Salaried/Exempt Anticipated Salary Range: $71,962.00 - $87,248.25 Security Clearance: Top Secret Level of Experience: Mid This opportunity…
Requisition Number: 30601 Required Travel: 0 - 10% Employment Type: Full Time/Salaried/Exempt Anticipated Salary Range: $90,873.00 - $103,001.60 Security Clearance: Secret Level of Experience: Mid This opportunity…
Senior individual-contributor role on BILL's AI Product Engineering team, architecting and shipping ML systems that turn unstructured financial documents into accurate financial data. The work spans LLM fine-tuning, document AI, model serving and evaluation, plus mentoring engineers and setting multi-quarter technical direction. Core stack: PyTorch and open-weight foundation models.
Senior machine learning scientist at Altos Labs designing, pre-training, and evaluating large-scale multimodal generative foundation models for biology — combining LLMs, graph neural networks, and knowledge graphs to accelerate research on aging and disease. Core stack: Python, PyTorch/JAX, and distributed multi-GPU training (FSDP, DeepSpeed).
A data engineer embedded within Trainline's Machine Learning team in London, building scalable data pipelines, feature stores, and cloud-native datasets that power ML models and analytics. Day-to-day work uses Python, SQL, Spark, Airflow, DBT, and AWS in a hybrid (60% office) setup.
Designs and deploys AI infrastructure for training biological foundation models, managing GPU clusters, storage, and MLOps tooling to support drug discovery research.
Designs and implements deep learning models to generate protein and antibody therapeutics using AI, collaborating with biologists and drug-discovery experts in a biotech startup.
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