Sr. Platform Engineering

Archer•San Jose, CA

8h•$144,000 - $175,000•Onsite

About The Position

Archer is an aerospace company based in San Jose, California, dedicated to building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. The company designs, manufactures, and operates an all-electric aircraft capable of carrying four passengers with minimal noise. Archer emphasizes diversity, equity, and inclusion in the workplace, believing it drives innovation and success. The company is seeking a highly motivated and versatile Senior Platform Engineer to join its platform engineering team. This pivotal role aims to accelerate product development and operational efficiency by bridging the gap between development, infrastructure, and AI/ML initiatives. The position focuses on automation, self-service infrastructure, and workflow optimization, ideal for an experienced engineer who excels at creating leverage for others and tackling complex, interdisciplinary challenges.

Requirements

5+ years of professional experience in Platform Engineering, DevOps, Site Reliability Engineering (SRE), or a related discipline.
Deep expertise in cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (Terraform strongly preferred).
Expertise in containerization and orchestration technologies (Docker and Kubernetes).
Proficiency in scripting and general-purpose programming languages (e.g., Python, Go, Bash).
Extensive experience designing and managing CI/CD systems at scale.
Proven track record of building internal developer platforms or self-service tools.

Nice To Haves

Experience with AI/ML infrastructure, MLOps tooling, or data pipeline technologies (e.g., Airflow, Kubeflow, Vertex AI, SageMaker).
Prior experience building conversational interfaces, bots, or workflow automation tools for platforms like Slack or Teams.
Familiarity with distributed tracing, logging, and monitoring systems (e.g., Prometheus, Grafana, ELK stack, Datadog).
Experience with security best practices in a cloud environment (IAM, network security, secrets management).

Responsibilities

Design, implement, and maintain scalable and reliable CI/CD pipelines (e.g., using GitLab CI, GitHub Actions, Jenkins, Spinnaker) to ensure rapid and safe deployments across multiple environments.
Drive the adoption of best practices for build management, testing automation, and deployment strategies (Canary, Blue/Green, etc.).
Minimize deployment friction and cycle time for all engineering teams.
Develop and champion self-service tooling and internal platforms (leveraging tools like Terraform, Ansible, Kubernetes, or equivalent cloud provider APIs) that empower development teams to provision and manage their own infrastructure resources securely and efficiently.
Establish guardrails and policy-as-code to ensure compliance, cost efficiency, and security across all provisioned infrastructure.
Collaborate closely with AI/ML Engineers to improve the underlying platform used for model training, experimentation, and serving.
Focus on the operationalization of MLOps pipelines, including data lineage tracking, feature store integration, and production model deployment automation.
Assist in optimizing resource allocation (e.g., GPU usage, specialized hardware) for AI workloads.
Architect and build automated workflows and bots, primarily within communication tools like Slack or Zoom, to streamline common engineering tasks (e.g., on-call handoffs, incident response, environment status checks, approval processes).
Develop internal APIs and services that connect disparate engineering systems to enhance communication and cross-functional transparency.