Top Machine Learning Engineer Jobs

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Own the machine learning platform supporting experiment tracking, distributed multi-node training, model evaluation, and production inference. Build reliable, reproducible, and cost-efficient systems that scale from small experiments to petabyte-scale multimodal training. Collaborate with research scientists, data and software engineers, and product teams to integrate models with downstream agents. Develop training and inference interfaces, support multimodal data, and optimize performance, observability, and infrastructure costs.

Develops AI-powered cybersecurity applications that protect enterprise systems, applications, and data. Responsibilities include integrating and maintaining threat defense and user access management capabilities, troubleshooting escalated incidents, investigating security issues, coordinating with managed security providers, and recommending corrective actions. The role involves cloud-native development, agentic AI, RAG, security controls, APIs, testing, monitoring, and software engineering best practices.

Build secure, scalable full-stack HR applications integrating enterprise AI services, LLMs, RAG pipelines, web technologies, and SQL data stores. Develop APIs, front ends, data tiers, retrieval and ETL pipelines, and AI-enabled workflows. Ensure data quality, privacy, role-based access, auditability, and performance. Collaborate with product, data, and security stakeholders to identify use cases and deliver reliable production solutions.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Build and optimize low-level software for large machine learning workloads across specialized, multi-vendor hardware. Tune model serving for speed, throughput, reliability, and cost; write GPU kernels; design and operate heterogeneous compute clusters; monitor production systems; and work directly with customers. The role requires deep computer architecture and operating systems knowledge, infrastructure or systems experience, strong communication, on-site availability in San Francisco, and a computer science degree. Exceptional results and GPU or ML serving experience are valued.

Deploy and operate machine learning, language, and speech services within secure DoD cloud environments. Migrate retrieval and modeling workflows, optimize model quality and performance, build structured evaluations, maintain release and data provenance records, and implement secure retrieval permissions and logging. The role requires production-scale model operations, IL5 experience, compliance collaboration, and full lifecycle ownership from concept through launch.

Build and improve large-scale machine learning models for aviation prediction products, including runway landing times, gate arrivals, and taxi durations. Develop high-performance systems using historical aviation data and complex data types, contribute ideas in an agile environment, and improve operational efficiency for airlines and airports. Preferred experience includes Kubernetes, AWS, GPU acceleration, graph neural networks, geospatial data, observability, ETL orchestration, and large-scale data processing.

Build and operate production-scale infrastructure for multimodal and video AI systems. Responsibilities include data and ingest pipelines, training and evaluation infrastructure, model inference and serving, vector retrieval, agentic orchestration, monitoring, security, and privacy. The role partners with research scientists, sets technical direction, mentors engineers, and scales reliable AI capabilities across cloud environments.

The Machine Learning Performance Engineer will build training and inference pipelines, enhance deep learning frameworks, improve performance, and collaborate with researchers and engineers to optimize trading systems.

Develop and operationalize scalable machine learning and advanced analytics solutions with business impact. Lead projects through the software development lifecycle, provide technical guidance, build prediction and optimization applications, contribute to ML platforms, and mentor teammates. The role requires collaboration with global AI/ML teams and expertise across cloud infrastructure, data engineering, MLOps, and software architecture.

Build full-stack infrastructure, DevOps, and MLOps tools for AI development teams. Responsibilities include CI/CD, reproducible environments, experiment and evaluation workflows, self-service applications, observability, cost tracking, deployment automation, identity and access services, LLM gateways, and usage metering. Collaborate directly with engineers and researchers to improve internal workflows, reliability, and operational efficiency.

Build and ship computer-vision models end-to-end: dataset curation, training, deployment, and monitoring across cloud and roadside edge hardware. Optimize for high-volume inference, accuracy, efficiency, and real-world robustness while collaborating cross-functionally and iterating rapidly.

Build and optimize end-to-end machine learning training infrastructure for large-scale multimodal robot data. Responsibilities include distributed training, data ingestion and transformation, checkpointing, orchestration, experiment tracking, sampling and curation, performance profiling, debugging data and training issues, and converting research prototypes into production infrastructure. The role may also involve robot learning, cloud and cluster infrastructure, post-training, on-robot inference optimization, CUDA or Triton kernels, and video-heavy datasets.

Train and evaluate learned manipulation policies for autonomous excavators using real fleet data and simulated rollouts. Explore diffusion and flow-matching architectures, conditioning, and hyperparameters; build evaluation pipelines for task success, smoothness, safety, and operator-likeness; investigate failure modes and sim-to-real issues; and contribute reproducible code while collaborating with behavior, controls, simulation, and autonomy teams.

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Designs, develops, fine-tunes, evaluates, and deploys machine learning and generative AI models, including LLMs, RAG systems, embeddings, and agents. Builds end-to-end ML pipelines, integrates Google Cloud data services, implements monitoring and drift detection, and applies responsible AI, security, governance, and compliance practices. Partners with stakeholders, data engineers, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions.

Design, develop, test, deploy, and operate AI-driven software and scalable infrastructure. Build APIs and third-party integrations, architect production systems, research emerging technologies, and provide technical leadership and mentorship. The role requires independent execution, collaboration with cross-functional teams, and work on mission-critical defense and intelligence applications.

Design, build, optimize, and scale machine learning infrastructure for training, evaluating, deploying, and serving large language models. Develop reusable workflow automation abstractions, distributed training and inference pipelines, model evaluation and monitoring frameworks, and latency optimizations. Collaborate with machine learning, data infrastructure, analytics, engineering, and product teams while establishing best practices for ML and data engineering.

Develop and productionize ML models across the full stack, optimizing training and inference for GPUs, NPUs, and AI silicon. Build inference runtimes, MLOps pipelines, embedded system integrations, verification harnesses, and security-focused ML capabilities. Collaborate with hardware, RTL, firmware, and QA teams to deploy AI features under real-world latency, memory, power, and reliability constraints.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Develop and ship computer vision and machine learning features for an AI-enabled CAD system. Responsibilities include data and labeling strategy, model selection and fine-tuning, evaluation pipeline development, 3D perception using LiDAR and imagery, production model adaptation, and customer-driven iteration. The role requires collaborating with product, engineering, customer-facing teams, and surveyors to deliver extraction workflows end to end.

Extend and scale in-house deep generative modeling tools for molecular design; run deep learning experiments to improve models or add functionality; and collaborate with software engineers to build efficient training and deployment systems for AI-driven protein therapeutics.

Build software connecting Fabri’s digital manufacturing process, including manufacturing data collection, process analytics, workflow automation, machine integration, backend services, and internal tools. Develop and deploy machine-learning models for outcome prediction, anomaly detection, inspection, and process monitoring. Analyze process data and geometry, improve manufacturing quality and throughput, and collaborate directly with operators, engineers, and manufacturing teams in an on-site factory environment.

Researches, designs, implements, and deploys machine learning algorithms for enterprise applications and federal customers. Collaborates with data scientists, software engineers, and subject matter experts to develop analytics capabilities, support application development, and deliver scalable solutions. Requires strong Python programming, applied machine learning, mathematical expertise, communication skills, and the ability to manage concurrent projects independently and collaboratively. Cloud, deep learning, NLP, computer vision, and reinforcement learning experience are desirable.