aion

Social Media Manager

• Own and manage aion's social media presence across LinkedIn, X, and other relevant channels.
• Develop and maintain the company content calendar.
• Monitor AI news, research, product launches, funding announcements, and industry trends.
• Identify opportunities for aion and leadership to participate in relevant conversations.
• Draft posts, threads, commentary, and thought leadership content for executives and company accounts.
• Work closely with design to create social graphics, carousels, videos, and visual content.
• Support conference, event, and speaking engagement promotion.
• Track performance metrics and continuously optimize content strategy.
• Build systems and workflows that allow aion to respond quickly to important developments in the AI ecosystem.

People Operations

• Partner with founders and leadership on organizational design and people strategy.
• Build scalable people processes for hiring, onboarding, performance, and retention.
• Drive initiatives that foster a strong, inclusive, and high-trust culture.
• Mentor and support managers in building high-performing teams.
• Oversee talent development, career growth programs, and engagement initiatives.
• Ensure compliance with employment laws and policies across regions.
• Continuously improve HR systems and processes to keep pace with aion’s growth.

Head of People Operations

Infrastructure Engineer

• Build and deploy comprehensive monitoring for GPU infrastructure using DCGM, NVML, and custom exporters; design metrics collection pipelines that track GPU health, utilization, thermal management, and performance across heterogeneous providers
• Deploy and manage production-scale Loki, Grafana, Tempo, and Mimir alongside Prometheus and Thanos/VictoriaMetrics; design retention strategies, aggregation rules, and query patterns that scale to thousands of GPUs
• Write Prometheus exporters in Go or Python for GPU metrics, platform services, and infrastructure components; implement proper metric naming, labeling strategies, and follow OpenMetrics standards
• Build custom controllers and operators for GPU workload management, scheduling, and resource allocation; instrument controllers with comprehensive metrics and tracing for observability
• Design and implement specialized observability for AI training workloads (GPU efficiency, distributed training performance, resource utilization) and inference services (latency percentiles, throughput, cost analytics)
• Deploy and manage SLURM clusters for HPC workloads, build observability for batch jobs, create bridges between SLURM and Kubernetes, and design unified monitoring across orchestrators
• Write and deploy systemd services for bare-metal GPU nodes including monitoring agents, metric collectors, and platform daemons; implement proper logging and error handling
• Manage infrastructure using ArgoCD and GitOps workflows, create observable platform abstractions that teams consume declaratively, build self-service capabilities with integrated monitoring
• Design intelligent, actionable alerting systems for GPU failures, thermal throttling, performance degradation, and workload anomalies; define platform SLOs and implement comprehensive monitoring to track reliability
• Build secure observability isolation ensuring customers access only their metrics and logs while maintaining platform-wide visibility for operations; implement query-time filtering and RBAC
• Implement automated collection of GPU and platform telemetry; integrate with OpenTelemetry for unified observability; manage cardinality and storage costs through intelligent aggregation
• Build monitoring pipelines that track GPU utilization, idle time, efficiency metrics, and cost allocation per tenant; create dashboards for platform economics and provider payout calculations
• Design systems that use observability data to detect hardware failures, trigger workload migration, and handle graceful degradation; ensure monitoring survives infrastructure changes
• Build intuitive dashboards, APIs, and alerting interfaces enabling providers to monitor hardware contributions and customers to track workload performance in real-time
• Use observability systems to rapidly identify root causes during production issues - GPU hardware failures, network bottlenecks, or workload problems; build tooling that reduces MTTD and MTTR
• Leverage observability data to identify bottlenecks in distributed training, inference latency issues, networking inefficiencies, and opportunities for GPU utilization improvement

Machine Learning Engineer

• Work directly at customer sites from factory floors to executive offices conducting discovery workshops and technical assessments to identify high-impact AI opportunities
• Design and architect end-to-end multimodal agent systems (voice + video + text) that leverage aion's distributed GPU infrastructure and managed services
• Build production-grade voice AI systems using STT, TTS APIs, and LLMs deployed on aion's platform
• Develop vision-enabled agents processing real-time video streams using computer vision pipelines on aion's infrastructure
• Implement sophisticated multi-agent orchestration with(or similar) frameworks like LangChain or LlamaIndex—enabling tool use, memory management, and autonomous task completion
• Rapidly prototype POCs in 2-4 weeks, coding alongside client teams to validate concepts and iterate based on feedback
• Optimize for sub-500ms latency, natural conversation flow, turn detection, and interruption handling in real-time systems
• Integrate agents directly into customer codebases via REST/GraphQL/WebSocket APIs and custom SDKs (Python, TypeScript)
• Act as trusted technical advisor to customers, shaping AI strategy and guiding roadmap decisions from concept to production
• Design data architectures with efficient processing pipelines and ingestion workflows for training and inference on aion's platform
• Implement RAG systems with vector databases optimizing embedding strategies, chunk sizes, and retrieval methods
• Prepare and validate datasets for fine-tuning, evaluation, and synthetic data generation
• Work with other MLEs, MLOps, SREs to carry out model deployment and productionization
• Implement LLM and agents observability and monitoring tracking token usage, latency, costs, and quality metrics across deployments on aion's infrastructure
• Instrument applications to trace LLM calls, retrieval operations, agent actions, and data flows
• Build evaluation frameworks with offline benchmarks (accuracy, relevance, safety metrics) and online monitoring (user feedback, drift detection)

Forward Deployed ML Engineer, Agents

Senior Forward Deployed ML Engineer, Agents

Full-stack Engineer

• Build and maintain platform services across aion's Compute and Inference platforms, working closely with senior engineers and platform leads
• Implement features for multi-cloud orchestration, resource scheduling, model deployment pipelines, and autoscaling systems
• Write well-maintained, production-grade code with proper abstractions, design patterns, and comprehensive test coverage
• Contribute to low-level design (LLD) including service APIs, database schema design, data models, and component interactions
• Collaborate with senior engineers on high-level design discussions, providing implementation perspectives and feasibility inputs
• Develop RESTful APIs and gRPC services for platform control planes, resource management, and inference serving
• Design and implement database schemas for storing platform state, resource metadata, billing data, and observability metrics
• Work with distributed storage systems, message queues (Kafka, RabbitMQ), and databases (PostgreSQL, Redis) to build reliable platform components
• Build event-driven architectures for asynchronous processing, job scheduling, and platform automation
• Implement monitoring, logging, and alerting for platform services to ensure production reliability
• Write comprehensive unit tests, integration tests, and end-to-end tests to ensure code reliability
• Participate in code reviews, providing constructive feedback and learning from senior engineers' perspectives
• Refactor existing code to improve maintainability, performance, and scalability
• Document design decisions, API specifications, and operational runbooks for platform services
• Debug production issues and contribute to incident response and post-mortems

Software Engineer

Platform Engineer

• Design and build aion's inference service platform the backbone for serving AI models at scale across diverse workloads
• Own and architect core platform components: AI Gateway, Resource Orchestrator, Runtime Engines, and Autoscaler
• Design highly modular, scalable, and extensible low-level designs (LLDs) for inference infrastructure components
• Lead high-level design discussions, establish architectural patterns, and drive technical decision-making for the inference stack
• Understand and optimize the dynamics of model deployment, version upgrades, and rollback strategies
• Build robust deployment pipelines for seamless model updates with zero-downtime deployments
• Design intelligent routing systems for multi-model serving, A/B testing, and canary deployments
• Implement strategies for efficient GPU utilization and model cold-start optimization
• Implement highly performant and optimized software for low-latency, high-throughput inference serving
• Build and debug production-grade code in distributed systems handling real-time AI workloads
• Optimize inference pipelines for latency, throughput, batching efficiency, and resource utilization
• Design fault-tolerant systems with graceful degradation and automatic recovery mechanisms
• Build high-performance telemetry and observability stack for inference metrics, performance tracking, and debugging
• Implement comprehensive monitoring for model latency, throughput, error rates, GPU utilization, and cost per inference
• Conduct thorough code reviews to maintain code quality, performance standards, and architectural consistency
• Establish engineering best practices for testing, documentation, and production readiness.