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Remote Autonomous Driving Engineer Jobs in Suffolk, VA

Stantec

Facility Assessor/Inspector (Remote)

Hampton, VA · On-site +1

$77K - $112K/yr

Must have valid driver's license with good driving record Education and Experience * Demonstrated ... Degree in architecture, engineering, construction management, or other applicable field * Master ...

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Remote Autonomous Driving Engineer information

See Suffolk, VA salary details

$56.1K

$130.6K

$186.9K

How much do remote autonomous driving engineer jobs pay per year?

As of Aug 14, 2026, the average yearly pay for remote autonomous driving engineer in Suffolk, VA is $130,623.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,600.00 and $186,500.00 per year, depending on experience, location, and employer.

What are the unique challenges of collaborating with global teams as a remote autonomous driving engineer?

As a Remote Autonomous Driving Engineer, you'll frequently collaborate with cross-functional teams spanning different time zones and regions. This can present challenges such as coordinating meetings, managing asynchronous communication, and ensuring clear documentation of technical requirements. To succeed, it's essential to develop strong written communication skills and utilize collaborative tools for version control, code review, and project management. Building relationships with colleagues remotely also requires proactive engagement and regular check-ins to maintain alignment on project goals.

What is a remote autonomous driving engineer?

A Remote Autonomous Driving Engineer is a professional who designs, develops, tests, and implements software and systems that enable vehicles to operate without direct human control. These engineers often work remotely, using cloud-based tools and simulations to collaborate with teams and test autonomous driving algorithms. Their responsibilities may include sensor integration, perception modeling, path planning, and ensuring safety and compliance with industry standards. They play a key role in advancing self-driving technology by working on cutting-edge machine learning, computer vision, and robotics challenges.

What is the difference between Remote Autonomous Driving Engineer vs Remote Autonomous Vehicle Software Developer?

AspectRemote Autonomous Driving EngineerRemote Autonomous Vehicle Software Developer
Required CredentialsEngineering degree, specialized in autonomous systems, certifications in robotics or AISoftware development background, experience with autonomous vehicle software, relevant certifications
Work EnvironmentCollaborates with hardware teams, field testing, simulation environmentsFocuses on coding, software testing, simulation, and integration
Industry UsageDesigns and tests autonomous driving systems, sensor integrationDevelops software components for autonomous vehicles, algorithms, and control systems

While both roles involve autonomous vehicle technology, the Remote Autonomous Driving Engineer focuses on system design, testing, and integration of autonomous driving systems, often working closely with hardware. The Remote Autonomous Vehicle Software Developer primarily concentrates on coding, software development, and simulation tasks within autonomous vehicle software platforms.

What are the key skills and qualifications needed to thrive as a remote autonomous driving engineer, and why are they important?

To thrive as a Remote Autonomous Driving Engineer, you need a strong background in robotics, computer vision, machine learning, and programming languages like Python or C++, often backed by a degree in engineering or computer science. Experience with simulation tools (e.g., ROS, CARLA), real-time data processing, and knowledge of automotive safety standards or certifications such as ISO 26262 are typically required. Excellent problem-solving skills, remote communication abilities, and adaptability are crucial soft skills for collaborating with distributed teams and addressing complex challenges. These competencies ensure the safe and efficient development of reliable autonomous driving systems in a remote work environment.
Remote Autonomous Driving Engineer jobs near you

Senior MLOps & Generative AI Engineer - Remote

Sentara Health

Virginia Beach, VA • Remote

$90K - $123K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired 2 days ago. Applications are no longer accepted.

Sentara Health rating

6.8

Company rating: 6.8 out of 10

Based on 411 frontline employees who took The Breakroom Quiz

494th of 887 rated healthcare providers

Job description

City/State Virginia Beach, VA Work Shift Multiple shifts available Overview: Sentara is hiring a Senior MLOps & Generative AI Engineer! This position is fully remote! Candidates must reside in one of the following states: Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, or Wyoming. Overview We are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence. This role combines two critical focus areas:
  • MLOps Engineering - building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.
  • Generative AI Engineering - designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.
As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization's AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments. Key Responsibilities MLOps Engineering Responsibilities
  • Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
  • Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
  • Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
  • Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
  • Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
  • Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
  • Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
  • Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
  • Identify gaps and improvement opportunities within the organization's ML platform ecosystem and architect scalable solutions to address them.
  • Support enterprise AI governance, compliance, auditability, and model risk management requirements.
  • Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
Generative AI Engineering Responsibilities
  • Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
  • Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
  • Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
  • Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
  • Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
  • Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
  • Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
  • Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
  • Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
  • Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
  • Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
  • Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
  • Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.
Required Qualifications
  • 5+ years of experience building and deploying production software, ML systems, or AI platforms.
  • 1+ years of hands-on experience building production Generative AI or LLM-based applications.
  • Strong programming skills in Python and experience with software engineering best practices.
  • Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
  • Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
  • Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
  • Experience deploying AI/ML systems in cloud environments including AWS, Azure, or GCP.
  • Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures.
  • Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure.
  • Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
  • Experience building monitoring, observability, and alerting solutions for ML and AI systems.
  • Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
  • Experience designing secure, scalable, production-ready AI platforms and services.
  • Strong communication and collaboration skills with the ability to work across technical and business teams.
Preferred Qualifications
  • Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
  • Experience working with EPIC or healthcare interoperability platforms.
  • Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
  • Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
  • Experience with GPU infrastructure optimization and scalable inference architectures.
  • Familiarity with multi-agent AI systems and autonomous workflows.
  • Experience with event-driven architectures, streaming pipelines, and real-time inference systems.
  • Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
  • Experience with enterprise AI platform architecture and internal developer platforms.
  • Prior experience mentoring engineers and leading technical initiatives.
Education
  • 5+ years of relevant experience with a degree (Required)
or
  • 7+ years of relevant experience without a degree (Required)
  • Experience in lieu of Bachelor's Degree.
Certification/Licensure
  • No specific certification or licensure requirements
Experience
  • 5 to 7 years of relevant experience
We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for Full Time employment is: $91,416.00 - $152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities. Keywords: Talroo-IT, MLOps, Gen AI, LLM, AWS, Azure, GCP, AI/ML, Python, PyTorch, Hugging Face Transformers, TensorFlow, RAG, EPIC, HIPAA, AI Governance Benefits: Caring For Your Family and Your Career • Medical, Dental, Vision plans • Adoption, Fertility and Surrogacy Reimbursement up to $10,000 • Paid Time Off and Sick Leave • Paid Parental & Family Caregiver Leave • Emergency Backup Care • Long-Term, Short-Term Disability, and Critical Illness plans • Life Insurance • 401k/403B with Employer Match • Tuition Assistance - $5,250/year and discounted educational opportunities through Guild Education • Student Debt Pay Down - $10,000 • Reimbursement for certifications and free access to complete CEUs and professional development •Pet Insurance •Legal Resources Plan •Colleagues have the opportunity to earn an annual discretionary bonus ifestablished system and employee eligibility criteria is met. Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves. In support of our mission "to improve health every day," this is a tobacco-free environment. For positions that are available as remote work, Sentara Health employs associates in the following states: Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.

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