Key responsibilities include:

• Leading design reviews and driving architecture within the team
• Defining and owning SLIs/SLOs and ensuring post-incident actions land and reliability improves release-over-release
• Implementing advanced optimizations such as micro-batch schedulers, speculative decoding, and KV-cache reuse
• Strengthening incident posture through capacity planning, autoscaling policy, and rollback/traffic-shift strategies
• Mentoring IC1/IC2 engineers and reviewing cross-team designs to elevate coding/testing standards

We're looking for someone with strong coding skills in Python or Go, deep familiarity with networked systems and performance, and hands-on experience with Kubernetes at production scale. If you have experience with inference internals, batching, caching, mixed precision, and streaming token delivery, that's a plus.

In addition to a competitive salary, we offer a range of benefits including medical, dental, and vision insurance, company-paid life insurance, and flexible PTO. We're committed to creating a work environment that's inclusive, diverse, and supportive of our employees' well-being.

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Key responsibilities include:

• Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.
• Define and own SLIs/SLOs; ensure post-incident actions land and reliability improves release-over-release.
• Implement advanced optimizations (e.g., micro-batch schedulers, speculative decoding, KV-cache reuse) and quantify impact.
• Strengthen incident posture: capacity planning, autoscaling policy, graceful degradation, rollback/traffic-shift strategies.
• Mentor IC1/IC2 engineers; review cross-team designs and elevate coding/testing standards.

Requirements include:

• 3-5 years of industry experience building distributed systems or cloud services.
• Strong coding in Python or Go (C++ a plus) and deep familiarity with networked systems and performance.
• Hands-on experience with Kubernetes at production scale, CI/CD, and observability stacks (Prometheus, Grafana, OpenTelemetry).
• Practical knowledge of inference internals: batching, caching, mixed precision (BF16/FP8), streaming token delivery.
• Proven track record improving tail latency (P95/P99) and service reliability through metrics-driven work.

Preferred qualifications include contributions to inference frameworks, experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies, and leading multi-team initiatives or partnering with customers on mission-critical launches.

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You will be responsible for shaping customer-facing capabilities while designing for scalability, extensibility, and performance of GPU training and adjacent areas. This will involve collaborating closely across the platform, product, infrastructure, and research organisations.

Key responsibilities include:

• Leading, mentoring, and growing a high-performing engineering team responsible for the Custom Training product and its foundational infrastructure
• Defining and owning the product and technical roadmap for AIR, balancing customer experience, functionality, and foundational investments
• Collaborating closely with product, research, platform, infrastructure teams, and customers to drive end-to-end delivery
• Driving architectural decisions and product design for managed GPU training at scale
• Advocating for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact

We are looking for someone with 8+ years of software engineering experience, with 3+ years in engineering management. You should have a track record of building and operating managed GPU training infrastructure at scale, as well as deep familiarity with distributed training frameworks and parallelism strategies.

In addition, you should have experience with training resilience patterns, such as checkpointing, elastic training, and automated failure recovery for long-running jobs. You should also have a strong understanding of GPU performance fundamentals, including NCCL, interconnect topologies, and memory optimisation.

Experience building platform products with clear SLAs is also essential, as is strong cross-functional leadership across platform, product, and research teams. Excellent collaboration and communication skills are also required.

The pay range for this role is $228,600-$314,250 USD per year, depending on location. The total compensation package may also include eligibility for annual performance bonus, equity, and benefits.

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