Innotechfy

TBC is building next-generation AI systems at the intersection of biological computing, generative models, and large-scale AI infrastructure. As we scale our world-model and neural-optimizer efforts, we are looking for an optimization-focused Research Scientist / ML Engineer to improve the efficiency, latency, throughput, and deployability of large models.

This role is focused on making frontier models run faster, cheaper, and more reliably — especially LLMs, diffusion models, video generation models, and world-model systems. You will work across inference optimization, training efficiency, model compression, memory management, and GPU-level performance to help turn research systems into scalable, customer-ready products.
WORK WITH USA 

What You’ll Work On

• Optimize inference for LLMs, diffusion models, video models, and world-model systems
• Improve serving efficiency through techniques such as KV caching, batching, quantization, distillation, speculative decoding, and memory optimization
• Build and optimize high-throughput inference pipelines for large models running on GPU clusters
• Profile model performance across latency, throughput, memory usage, GPU utilization, and cost
• Implement custom kernels or low-level optimizations using Triton, CUDA, PyTorch, or related systems
• Improve training and fine-tuning efficiency for large generative models, including distributed training, checkpointing, parallelism, and data loading
• Work with research teams to identify bottlenecks in model architecture, inference paths, and deployment workflows
• Translate model performance improvements into clear customer-facing benchmarks and technical proof points
• Evaluate trade-offs across model quality, latency, cost, memory, and deployability

What We’re Looking For

• Strong background in machine learning systems, model optimization, or high-performance AI infrastructure
• Hands-on experience optimizing LLMs, diffusion models, video generation models, or other large generative systems
• Experience with one or more of:
  • Inference optimization
  • KV caching / attention optimization
  • Triton or CUDA kernel development
  • Quantization, pruning, distillation, or model compression
  • Distributed training / fine-tuning efficiency
  • GPU profiling and performance debugging
• Strong PyTorch experience and comfort working close to the model/runtime boundary
• Ability to reason about trade-offs between quality, latency, throughput, memory, and cost
• Comfortable working across research code, production systems, and benchmarking infrastructure
• Excited to work in an ambiguous, early-stage environment where optimization work directly shapes product feasibility

What Success Looks Like

• Large models run faster, cheaper, and more reliably across TBC’s core workloads
• Inference pipelines show measurable improvements in latency, throughput, memory use, and GPU utilization
• Training and fine-tuning workflows become more efficient, reproducible, and scalable
• Optimization work translates into clear product and customer value, not just internal benchmarks
• Research prototypes become deployable systems that can support demos, evaluations, and early partner use cases

Preferred Qualifications

• PhD, MS, or equivalent industry experience in Computer Science, Machine Learning, Systems, Robotics, or related field
• Prior work optimizing large-scale generative models in production or research settings
• Experience with modern inference/training stacks such as PyTorch, Triton, CUDA, vLLM, TensorRT, DeepSpeed, FSDP, Ray, or similar tooling
• Experience working with LLMs, diffusion models, video generation models, or world models

Client: US-based enterprise (Salesforce platform)
Engagement model: Dedicated outstaffing, 1 engineer (may expand — TBC)
Duration: Long-term expected (TBC)
Location: Remote, EU preferred (or comparable time-zone-flexible geography)
Target start: ASAP — shortlist to be delivered in 2–3 weeks

Time zone (HARD requirement):
Must be comfortable working in overlap with California business hours — approx. schedule 6 PM–2/3 AM CET / EET. Confirm comfort with this schedule at the very first screening call. If candidate is not comfortable → reject, do not proceed.

Must-have skills:

• Proven hands-on experience with Intelligent Document Processing (IDP) — parsing complex, unstructured, long-form business documents (contracts / authorizations / similar)
• Strong MuleSoft expertise — Anypoint Platform, integration flows, connectors
• Experience integrating MuleSoft with Salesforce
• Enterprise-grade project background (not startup-only)
• Strong spoken English, comfortable in direct communication with US clients

Nice-to-have:

• Experience with AI/ML-based document extraction tools (Amazon Textract, Google Document AI, Azure AI Document Intelligence, ABBYY, or similar)
• Experience joining a project mid-flight, reviewing existing architecture, proposing refactoring
• Healthcare / insurance / regulated-industry background (unconfirmed, client references "authorizations")