Staff Engineer (Core & MLOps)

Role Overview

Accountabilities

• Architect and evolve the control and context planes, advancing service and schema registries, SLO enforcement, health-aware routing, automated canary releases, and operational feedback loops.
• Own the service chassis and golden path, maintaining and improving multi-language Java and Python client libraries, standardized workload specifications, Helm charts, and deployment pipelines.
• Define and govern inter-service contracts, including gRPC and Protocol Buffer definitions, API gateway transcoding, versioning policies, and schema evolution standards.
• Operate and improve the core platform infrastructure across Kubernetes, Terraform, HAProxy/Nginx, Confluent Kafka, real-time billing pipelines, Valkey, and database modernization initiatives.
• Lead architectural strategy through Requests for Discussion (RFDs) covering workflow orchestration, gateway orchestration, multi-cluster routing, automated failover, and other critical platform initiatives.
• Establish reliability engineering practices, including SLOs, SLIs, error budgets, fault isolation, and automated weighted canary deployments.
• Participate in shared infrastructure on-call rotations, lead incident post-mortems, and convert operational insights into platform improvements.
• Mentor engineers across multiple squads, review architectural proposals, and establish engineering practices that make reliable software development more consistent and efficient.

Requirements

• 10+ years of experience building scalable distributed backend systems, with a strong track record of creating internal platforms or core libraries adopted across engineering organizations.
• Advanced Java expertise, including reactive frameworks such as Vert.x or Netty, combined with strong Python proficiency.
• Deep experience with gRPC and Protocol Buffers, including schema evolution and backward compatibility in mission-critical systems.
• Hands-on production experience with Kubernetes at scale, Terraform, and event-streaming platforms such as Kafka.
• Experience designing automated telemetry pipelines, materialized views, feature stores, or other feedback systems that use production data to dynamically improve system behavior.
• Strong reliability engineering background, including SLO/SLI definition, blast-radius analysis, fault tolerance, and rigorous service contracts.
• Exceptional technical writing skills and the ability to communicate complex architectural concepts clearly while driving alignment across teams.
• Strong written and interpersonal communication skills suited to a globally distributed, remote-first environment.
• A curious, continuous-learning mindset with an interest in evaluating new technologies, architectures, and engineering approaches.

Preferred Qualifications

• Experience with Temporal, DBOS, or similar durable execution platforms is a plus.
• MLOps experience, including model serving, performance monitoring, or production drift detection, is advantageous.
• Familiarity with zero-trust networking and service meshes such as SPIRE, mTLS, Cilium, Istio, or Envoy is beneficial.
• Experience building developer tooling such as CLIs, SDKs, or project generators is a plus.
• Experience with large-scale web scraping or crawling, or contributions to distributed-systems and data-extraction open-source projects, is advantageous.

Benefits

• Fully remote, remote-first working environment with flexible working hours.
• Freedom and flexibility to work from the location where you are most productive.
• Opportunity to work on core infrastructure supporting large-scale web data pipelines and distributed systems.
• Exposure to cutting-edge open-source technologies, tools, and evolving AI and web data infrastructure.
• Opportunities to attend conferences and connect with colleagues across the globe.
• Collaboration with a diverse, multicultural, and globally distributed engineering community.
• High level of autonomy and organizational trust.
• Opportunities to influence platform architecture, engineering standards, and technical strategy across multiple teams.

Application Process

• Jobgether uses an AI-powered matching process to review applications quickly, objectively, and fairly against the role's core requirements.
• The system identifies top-fitting candidates, and this shortlist is shared directly with the hiring company.
• The final decision and next steps (interviews, assessments) are managed by the hiring company's internal team.
• Jobgether will process personal data to evaluate candidacy and share relevant information with the hiring employer under applicable data protection laws.