Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.
This is a remote position.
We’re at the cutting edge of artificial intelligence, building intelligent solutions that transform data into actionable insights. By leveraging advanced machine learning algorithms, deep learning frameworks, and scalable architectures, we deliver AI-powered applications that drive innovation and business growth. To help us achieve this, we are seeking an experienced AI & ML Engineer. The ideal candidate will be highly skilled in developing, training, and deploying machine learning models, and will be committed to turning complex datasets into practical, real-world solutions.
Requirements
Strong experience in AI/ML model development and deployment using frameworks such as TensorFlow, PyTorch, or scikit-learn.
Proficiency in programming languages such as Python, R, or Java.
Hands-on experience with data preprocessing, feature engineering, and model evaluation techniques.
Knowledge of deep learning architectures including CNNs, RNNs, Transformers, and large language models.
Experience with cloud platforms (AWS, Azure, GCP) for training and deploying ML models.
Ability to collaborate with data engineers, MLOps engineers, and business stakeholders to deliver AI solutions.
Strong problem-solving, analytical, and research skills, with a commitment to staying updated on AI advancements.
Similar Jobs
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.
Top Skills:
AWSAzureAzure Machine LearningCi/CdCloud ComputingComputer VisionData PipelinesDatabricksDeep LearningDistributed ComputingFoundation ModelsGCPLarge Language ModelsMachine LearningMlopsNlpPysparkPythonSnowflakeSQL
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Leads the design, deployment, and operation of enterprise AI/ML, AIOps, and Digital Twin capabilities. Drives predictive analytics, intelligent automation, incident prevention, root-cause analysis, autonomous remediation, observability, and SRE practices across a mission-critical healthcare platform. Leads major incident response, mentors engineering teams, evaluates emerging technologies, and establishes scalable operational and reliability strategies.
Top Skills:
Agentic AiAi/MlAiopsAnsibleArmAWSAzureAzure DevopsAzure MonitorBicepCi/CdDatadogDigital TwinDockerDynatraceGCPGenerative AiGithub ActionsGrafanaJavaJenkinsJsmKubernetesLarge Language Models (Llms)OpenshiftPagerdutyPowershellPrometheusPythonServicenowShell ScriptingSite Reliability Engineering (Sre)SplunkTerraformXmatters
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Own the architecture and long-term evolution of hybrid, multi-tenant AI compute platforms across bare-metal OpenShift and public-cloud services. Define distributed training networking, GPU utilization and cost models, GitOps governance, model-serving standards, workload placement, identity and security controls, SLOs, disaster recovery, and platform upgrade strategies. Partner with AI, privacy, and security teams to deliver HIPAA-compliant infrastructure for training and inference workloads.
Top Skills:
Argo CdAws BedrockAzure Ai FoundryCephDcgmDeepspeedFsdpGcp Vertex AiGitopsGpudirect RdmaHipaaIbm Storage ScaleInfinibandJaxKserveKubeflow PipelinesKubernetesKueueLustreMigMtlsNcclNfdNvidia Gpu OperatorNvidia GpusNvlinkOauthOdfOidcOpenshift AiPytorch DdpRayRbacRed Hat OpenshiftRhacmRocev2Tensorrt-LlmVastVaultVllmVolcanoWeka
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
