Forward Deployed Engineer - Physical AI Cloud Platform
New
Fully remote flexibility within the United States.Full-TimeSenior
SalaryCompetitive base compensation ranging from $179,500 to $224,300 USD.
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Job Details
- Experience
- 6+ years of hands-on engineering experience in backend development, cloud infrastructure, platform engineering, SRE, or related fields; at least 2 years of experience in customer-facing or deployment-focused technical roles.
- Required Skills
- PythonKubernetesGoCI/CDDistributed Systems
Requirements
- 6+ years of hands-on engineering experience in backend development, cloud infrastructure, platform engineering, SRE, or related fields.
- At least 2 years of experience in customer-facing or deployment-focused technical roles such as Forward Deployed Engineer, technical lead, founding engineer, or equivalent.
- Strong experience building distributed systems, compute platforms, internal developer platforms, orchestration systems, or ML infrastructure.
- Advanced programming skills in Python, Go, or similar backend and systems programming languages.
- Experience using AI-native development tools such as Claude Code, Codex, Cursor, or similar technologies.
- Strong knowledge of cloud-native technologies including Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.
- Familiarity with GPU workloads, HPC environments, ML training pipelines, inference systems, or large-scale compute workloads.
- Proven ability to troubleshoot complex infrastructure issues across multiple technology layers.
- Strong understanding of security, reliability, workload isolation, access controls, and operational best practices.
- Ability to operate independently in ambiguous environments with strong ownership and problem-solving skills.
- Excellent communication skills with the ability to collaborate with technical leaders, customers, and cross-functional engineering teams.
Responsibilities
- Own end-to-end technical execution for strategic customer and partner engagements, including discovery, infrastructure design, implementation, and production deployment.
- Design and build cloud infrastructure supporting advanced AI workloads, including simulation, training, evaluation, inference, and large-scale batch processing.
- Develop platform services for workload execution, scheduling, observability, logging, secrets management, access control, and cost optimization.
- Create secure and scalable onboarding environments for customers, including sandbox setups, data storage, workflow execution, and production deployment capabilities.
- Improve platform reliability, security, performance, and cost efficiency by debugging issues across application, network, storage, compute, and orchestration layers.
- Collaborate with engineering teams to expose infrastructure capabilities through APIs, SDKs, and product workflows.
- Define long-term infrastructure architecture for multi-tenant SaaS platforms, enterprise deployments, and high-throughput AI workloads.
- Identify recurring customer infrastructure challenges and transform them into reusable platform capabilities.
- Use modern AI development tools to accelerate engineering workflows, improve productivity, and deliver high-quality production software quickly.
- Create technical documentation, reference architectures, and feedback loops that help improve products and support broader engineering teams.
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