Sr. Platform Engineering

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the

Benefits

of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success.

We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members. We are looking for a highly motivated and versatile Senior Platform Engineer to join our platform engineering team. This unique role is central to accelerating our product development lifecycle and operational efficiency.

You will be instrumental in bridging the gap between development, infrastructure, and our burgeoning AI/ML initiatives, focusing on automation, self-service infrastructure, and workflow optimization. This position is ideal for an experienced engineer who thrives on creating leverage for others and tackling complex, interdisciplinary challenges.

Key Responsibilities

, using GitLab CI, GitHub Actions, Jenkins, Spinnaker) to ensure rapid and safe deployments across multiple environments. ). Minimize deployment friction and cycle time for all engineering teams.

Self-Service Infrastructure & Provisioning 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. AI Infrastructure Enhancement & Support 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. , GPU usage, specialized hardware) for AI workloads. , 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.

Required Qualifications

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).

, 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.

Preferred Qualifications

, Airflow, Kubeflow, Vertex AI, SageMaker). Prior experience building conversational interfaces, bots, or workflow automation tools for platforms like Slack or Teams. , Prometheus, Grafana, ELK stack, Datadog).