ML, FPGA, Backend Optimization Experts
Budget: $10 – $30 USD
I’m expanding Eigen Computing’s stealth-mode R&D team and need senior-level talent who can tackle optimisation at scale. We are building a hardware-accelerated probabilistic engine that runs QUBO/Ising workloads for Logistics, Finance, and Cyber-Intelligence, and I’m open to engaging specialists across several tracks:
• ML Engineer – craft graph neural networks and matrix-compression pipelines that translate complex optimisation problems into sparse, hardware-friendly representations.
• FPGA Engineer – write VHDL/Verilog kernels for AWS F1, pushing the solver to micro-second latency.
• Backend Architect – design a high-performance API layer in Go, Rust or Python that orchestrates FPGA instances, manages job queues and exposes REST/gRPC endpoints.
• Cyber-Security Expert – conduct cryptanalysis and network-intelligence research to harden the solver and uncover new optimisation attack vectors.
If you span multiple areas—graph ML, HDL, and low-latency backend work in particular—even better.
Deliverables
– Modular, well-documented code that builds and runs on AWS F1.
– Benchmark results showing throughput, latency and accuracy gains on logistics, finance and cyber-intel datasets.
– A concise technical memo outlining assumptions, tooling and next steps.
– Continued collaboration through agile sprints and GitLab pull requests.
What I’m looking for
• Hands-on success with hardware acceleration, large-scale optimisation or neural-network compression.
• Ability to thrive under NDA in a fast-moving, stealth environment.
• Clear written English and reliable participation in remote stand-ups.
Next steps
Review our vision at www.eigencomputing.com, sign the NDA I’ll send on award, and let me know which roles you can cover. Highlight past logistics optimisation, financial modelling or cyber-intelligence projects where you accelerated performance—links, papers or repos welcome.
Let’s push the boundaries of optimisation together. If you are interested text equity.
• ML Engineer – craft graph neural networks and matrix-compression pipelines that translate complex optimisation problems into sparse, hardware-friendly representations.
• FPGA Engineer – write VHDL/Verilog kernels for AWS F1, pushing the solver to micro-second latency.
• Backend Architect – design a high-performance API layer in Go, Rust or Python that orchestrates FPGA instances, manages job queues and exposes REST/gRPC endpoints.
• Cyber-Security Expert – conduct cryptanalysis and network-intelligence research to harden the solver and uncover new optimisation attack vectors.
If you span multiple areas—graph ML, HDL, and low-latency backend work in particular—even better.
Deliverables
– Modular, well-documented code that builds and runs on AWS F1.
– Benchmark results showing throughput, latency and accuracy gains on logistics, finance and cyber-intel datasets.
– A concise technical memo outlining assumptions, tooling and next steps.
– Continued collaboration through agile sprints and GitLab pull requests.
What I’m looking for
• Hands-on success with hardware acceleration, large-scale optimisation or neural-network compression.
• Ability to thrive under NDA in a fast-moving, stealth environment.
• Clear written English and reliable participation in remote stand-ups.
Next steps
Review our vision at www.eigencomputing.com, sign the NDA I’ll send on award, and let me know which roles you can cover. Highlight past logistics optimisation, financial modelling or cyber-intelligence projects where you accelerated performance—links, papers or repos welcome.
Let’s push the boundaries of optimisation together. If you are interested text equity.