✓ Screened eligible
Why you can actually get this job
The posting states 'Remote (world)' with no geographic, work authorization, timezone, language, or residency restrictions. It is a full-time employee role with no visa sponsorship exclusions stated. The role is explicitly open globally and contains no gates that would disqualify the target user.
“Remote (world)”
Geographic scope — Explicit worldwide remote designation indicates global hiring scope with no geographic restriction.
“Competitive compensation + meaningful equity at Series A”
Work authorization — No visa sponsorship exclusion or work authorization requirement stated in a full-time employee role, permitting hiring of non-US authorized candidates.
“Remote (world)”
Physical presence — Remote role with no physical presence, office days, relocation, or commuting requirements stated.
About the Role
We’re looking for a Machine Learning Engineer to own and push the limits of model inference performance at scale . You’ll work at the intersection of research and production—turning cutting-edge models into fast, reliable, and cost-efficient systems that serve real users.
- This role is ideal for someone who enjoys deep technical work, profiling systems down to the kernel/GPU level, and translating research ideas into production-grade performance gains.
What You’ll Do
- Optimize inference latency, throughput, and cost for large-scale ML models in production
- Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)
Implement and tune techniques such as
- Quantization (fp16, bf16, int8, fp8)
KV-cache optimization & reuse
- Speculative decoding, batching, and streaming
- Model pruning or architectural simplifications for inference
- Collaborate with research engineers to productionize new model architectures
- Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks)
- Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups
- Improve system reliability, observability, and cost efficiency under real workloads
What We’re Looking For
- Strong experience in ML inference optimization or high-performance ML systems
- Solid understanding of deep learning internals (attention, memory layout, compute graphs)
- Hands-on experience with PyTorch (or similar) and model deployment
- Familiarity with GPU performance tuning (CUDA, ROCm, Triton, or kernel-level optimizations)
- Experience scaling inference for real users (not just research benchmarks)
- Comfortable working in fast-moving startup environments with ownership and ambiguity
Nice to Have
- Experience with LLM or long-context model inference
- Knowledge of inference frameworks (TensorRT, ONNX Runtime, vLLM, Triton)
- Experience optimizing across different hardware vendors
- Open-source contributions in ML systems or inference tooling
- Background in distributed systems or low-latency services
Why Join Us
- Real ownership over performance-critical systems
- Direct impact on product reliability and unit economics
- Close collaboration with research, infra, and product
- Competitive compensation + meaningful equity at Series A
- A team that cares about engineering quality, not hype
Originally posted on Himalayas
