Staff Generative AI Researcher, World Models
General Motors
Lead research into diffusion-based world models for General Motors’ autonomous vehicles. Advance generative AI, simulation, and Level 3 autonomy through large-scale research.
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Accenture Federal Services oferă servicii de tehnologie și consultanță guvernului federal al Statelor Unite. Ca parte a Accenture, compania activează în domeniile consultanței, apărării și logisticii, ajutând agențiile federale să răspundă unor nevoi operaționale și de misiune complexe prin cloud computing, securitate cibernetică, date și inteligență artificială, inginerie digitală și alte tehnologii emergente. Activitatea sa include dezvoltarea unor sisteme digitale sigure și scalabile, utilizarea informațiilor bazate pe date pentru îmbunătățirea procesului decizional și modernizarea infrastructurii guvernamentale. Compania se bazează, de asemenea, pe cercetare și dezvoltare și pe design centrat pe oameni pentru a sprijini operațiuni reziliente și eficiente în sectorul public.
AI/ML Engineer – Remote in Virginia
Design and deploy Vertex AI and generative AI solutions for U.S. federal agencies. Build production machine learning pipelines, intelligent agents, and secure, governed cloud architectures.
Descriere
- Work with stakeholders to define AI and machine learning use cases and convert business requirements into technical solutions
- Design, develop, fine-tune, and assess machine learning and generative AI models using Vertex AI, Gemini, LLMs, RAG, embeddings, deep learning, and open-source tools
- Build complete machine learning pipelines for data ingestion, feature engineering, orchestration, and model and prompt CI/CD
- Deploy scalable models and agents while overseeing monitoring, drift detection, and production issue resolution
- Partner with data engineering teams to develop reliable data architectures using BigQuery, Dataflow, Pub/Sub, and Feature Store
- Apply Responsible AI, security, governance, and compliance practices, including IAM, encryption, and auditing
- Coordinate with product owners, platform teams, DevOps/SRE professionals, and junior engineers to deliver dependable AI systems
- Conduct experiments, prototypes, exploratory analysis, hyperparameter tuning, and documentation for pipelines and workflows
Cerințe
- U.S. citizenship and eligibility for Public Trust clearance
- 3–6 or more years of experience in machine learning engineering, data science, or AI development
- At least 3 years of experience leading technical teams toward defined objectives and outcomes
- Experience creating and implementing technical standards, systems, and processes across cloud and on-premises environments
- Demonstrated expertise in recommending technology strategies and making technical decisions
- Experience providing technical guidance and support to maintain compliance with standards and policies
- Experience with Google Storage capabilities, including access control, versioning, encryption, lifecycle management, logging, backups, static files, machine learning workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, and Persistent Disk
- Proficiency in Python and SQL
- Experience with TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine-tuning, and RAG architectures
- Experience with Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, and IAM
- Experience with Vertex AI Pipelines, Dataflow, Pub/Sub, and Feature Store
- Experience with Vertex AI Search, AI agents, RAG solutions, or vector databases such as Vertex Vector Search, Pinecone, or Milvus
- Experience deploying AI workloads through Kubernetes or microservices architectures
- Google Cloud Professional certification in Machine Learning Engineer, Data Engineer, or Cloud Architect
- Hands-on experience with Vertex AI, Gemini APIs, or comparable cloud AI and machine learning platforms
- Advanced Python development skills and familiarity with machine learning frameworks
- Strong knowledge of LLMs, embeddings, vector search, and generative AI methods
- Authorization to work in the United States without current or future visa sponsorship
- Preferred: knowledge of Responsible AI, bias mitigation, and model interpretability
- Preferred: familiarity with GCP operational services such as IAM, KMS, Logging/Monitoring, VPC, and Cloud Storage
- Preferred: exposure to AWS or Azure equivalents and third-party security, observability, or DevOps tools
- Preferred: master’s degree and experience in federal or regulated industries
Beneficii
- Supportive, collaborative workplace community
- Practical, hands-on project experience
- Professional certification opportunities
- Industry-focused training
- Broad benefits offering
- Reasonable accommodations for disabilities or religious observances during recruitment and employment
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