Senior AI Infrastructure Engineer

A strong engineering foundation. Hands-on AI infrastructure experience. At least 1 year building and operating infrastructure for large language models, including model deployment, inference serving, or distributed training. You can design the system, write the code, and own it in production.

Production model deployment experience. You’ve deployed and maintained LLMs or other demanding ML workloads using engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or comparable systems. You understand the work between loading a model and running a reliable service.

GPU cluster and orchestration experience. You’ve managed GPU workloads using Kubernetes, Slurm, or an equivalent platform, with practical experience in scheduling, resource allocation, capacity planning, and failure recovery.

Performance debugging skills. You can use traces, metrics, and profiling tools to distinguish compute, memory, communication, and scheduling bottlenecks. You understand how batch size, context length, precision, and multi-GPU execution affect performance and cost.

Strong software and systems skills. You’re proficient in Python and comfortable with backend or systems development in Go, C++, Rust, or a comparable language. You have practical experience with Linux, containers, deployment automation, and distributed services.

Operational ownership. You’ve owned production incidents, built useful monitoring, and made releases recoverable. You can explain the trade-offs behind a design and work effectively with researchers, product engineers, and infrastructure partners.

Experience with distributed training frameworks such as PyTorch FSDP or Megatron, or infrastructure for reinforcement learning and rollout generation.

Experience tuning inference engines, serving MoE models, or implementing quantization, speculative decoding, and prefill/decode disaggregation.

Familiarity with GPU interconnects, NCCL, RDMA, topology-aware scheduling, or diagnosing multi-node communication problems.

CUDA or Triton kernel development, contributions to AI infrastructure projects, or experience building cluster operators and scheduling integrations.

Experience operating infrastructure across multiple regions or providers, particularly for healthcare or other sensitive production workloads.

Build for the next decade, not next quarter . Our targets are outrageous on purpose. The world's health doesn't have the luxury of incrementalism.

Lead, don't wait . We treat tomorrow's problems today. Sometimes we build what's needed before it's wanted, and we're fine with that.

Follow the evidence . Trust the patient. We pursue truth relentlessly. But when the subjective and objective disagree, we treat the patient, not the numbers. Ego is a comorbidity we can't afford.

Own the outcome . Everyone here carries the company. Raise problems with solutions, solve them end-to-end, and never be a bystander.

Ship, measure, go again . A button today, a workflow tomorrow. More iterations beat better planning. We're precise at pace, not reckless.

Live in clinicians' reality . Not the ideal workflow, the twenty-patients-before-lunch actual one. We build for exhausted humans, and we'd better be decent ones while we do it.

You’ll join a team focused on real-world impact over imaginary valuations and glossy PR. We live and breathe the challenges of modern health systems, and are laser-focused on exacting the change we’d like to see. We’re medicos, engineers, builders, and designers who’ve felt the moral and practical toll of what non-care feels like. True A-players progress extremely fast here.

The nature of the scale-up game is demanding, but we value sustainable performance and mental health. You're trusted to perform, and you set your schedule. We operate on outcomes > inputs, not process theatre. We all take the bins out, metaphorically and literally.

Building what we’re building isn’t always easy. But we didn’t choose easy, we chose to build something that actually matters. We hold ourselves to a higher standard because healthcare demands it. If you join Heidi, you recognise that the deeper question isn’t whether AI can solve the global healthcare crisis, but whose hands will shape it. The work is hard, but you will trust and admire the people you work beside, and rest easy knowing you’re doing the defining work of your career.

We offer a $1,000 annual learning and development budget, a $150/month health and wellness allowance, a $500 home office budget, 26 weeks paid primary parental leave and 18 weeks paid secondary parental leave, fertility support up to $10,000, four weeks of work from anywhere per year, and serious equity.