Top Tech Jobs & Startup Jobs

Own the deployment strategy and roadmap for moving trained and fine-tuned AI models into reliable production use. Define serving workflows, autoscaling, monitoring, rollback, incident response, SLAs, pricing inputs, and API/SDK experiences. Partner with infrastructure, research, engineering, GTM, and users to prioritize improvements, resolve dependencies, guide launches, analyze performance and cost tradeoffs, and build a scalable deployment platform.

Leads senior and staff engineers building reliable, performant ML infrastructure and product systems. Owns architecture, system design, technical direction, execution, roadmap prioritization, design reviews, prototyping, and critical issue resolution. Partners with researchers and product teams, hires senior talent, and mentors engineers into technical leaders.

Research and build large-scale pre-training data systems for frontier AI models. Responsibilities include sourcing, curating, filtering, and evaluating text, code, and multimodal datasets; developing quality metrics; scaling reproducible data pipelines; mitigating privacy, safety, and licensing risks; analyzing downstream model effects; and publishing research and tools.

Conduct research and engineering on multimodal AI, focusing on visual perception, vision-language interaction, model architectures, large-scale datasets, evaluation benchmarks, and distributed training. The role involves designing experiments, analyzing model performance, collaborating across research and product teams, publishing findings, and contributing high-performance code to frontier multimodal systems.

Own the reliability, performance, and uptime of large-scale post-training and reinforcement learning systems. Debug distributed failures across accelerators, networking, storage, schedulers, and training frameworks; build monitoring, alerting, recovery, checkpointing, and scheduling tools; improve cluster utilization and fault tolerance; support production model runs through on-call rotations and postmortems; and partner closely with research teams during active training runs.

Own and evolve security infrastructure for AI foundation-model platforms across compute, storage, networking, and data systems. Architect secure Kubernetes and cloud patterns, manage identity and secrets, secure data pipelines, create threat models, and automate policy enforcement and security checks. Partner with research and engineering teams to deliver scalable systems with strong security, reliability, and performance.

Design, build, and operate large GPU supercomputing clusters supporting large-scale AI training and inference. Responsibilities include cluster provisioning, automation, scheduling, orchestration, capacity planning, storage and artifact management, monitoring, reliability improvements, and performance optimization. The engineer will partner with researchers on scaling runs and advise on distributed training and infrastructure trade-offs.

Design and optimize high-performance ML kernels for large-scale language model training. Develop CUDA, CuTe, and Triton implementations for attention, matrix multiplication, gating, and normalization; improve memory efficiency, low-precision arithmetic, distributed compute, infrastructure stability, and hardware utilization. Collaborate with researchers and systems architects, benchmark performance across GPU generations, and contribute reusable kernel libraries, technical documentation, and open-source or research work.

Coach managers and advise leadership on organizational design, succession planning, team dynamics, and talent decisions. Design scalable compensation, career-leveling, feedback, and evaluation systems for a high-growth AI research lab. Support researcher and engineer performance, development, retention, and growth while ensuring employment practices align with North American law and company values.

Owns the engineering and operation of a large macOS endpoint environment. Responsibilities include MDM platform management and migration, configuration as code, zero-touch provisioning, patching, software distribution, fleet telemetry, compliance, endpoint security, device trust, automated remediation, and advanced troubleshooting. The role also leads technical initiatives, architecture decisions, progressive delivery, and integrations across IT, Security, Identity, and Infrastructure.