Field Applications Software Engineer

Synthara AG; Zürich; On-site; CHF 110,000 - 170,000; Full time

Job summary

Synthara AG is seeking a hands-on, cross-functional engineer to demonstrate and deploy ComputeRAM-enabled workloads on customer systems, spanning AI, signal processing, and cryptography. You will work where ML, embedded systems, and hardware intersect, analyzing workloads, benchmarking performance, and guiding product development while supporting customer engagements.

The role emphasizes collaboration with customers and internal teams and requires a strong background in ML/AI architectures and

Qualifications

• Strong understanding of ML/AI architectures with hands-on experience in PyTorch or TensorFlow.
• Understanding of SoC and computer architectures or embedded software.
• Ability to read technical papers and translate into working implementations.
• Clear technical writing and communication skills.
• Comfort with ambiguity and cross-disciplinary collaboration with customers and teams.
• Strong interpersonal skills for customer engagement.
• Degree in CS, E/E, ML, applied math, physics, engineering or related field.

Responsibilities

• Implement and benchmark AI, ML, and signal-processing applications on ComputeRAM-enhanced systems.
• Develop proofs-of-concept for prospective customers from analysis to benchmarking and presentation.
• Collaborate with hardware, architecture and software teams to inform the roadmap.
• Produce technical documentation, benchmark reports, and collateral.

Skills

ML/AI architectures

SoC architecture

Technical writing

Cross-functional collaboration

Customer interaction

Education

Degree in computer science, electrical/electronic engineering, ML, applied mathematics, physics, engineering or related field

Tools

PyTorch

TensorFlow

Job description

Seniority: 3+ years relevant industry/research experience, or equivalent advanced technical background

About the role

Join Synthara's applications team and help demonstrate and deploy ComputeRAM®s on customer systems and AI, signal-processing and cryptography workloads.

You will work at the intersection of machine learning, signal-processing, embedded systems, computer architecture, compilers, simulators and customer applications. You will analyse and implement representative workloads, map their computational operations onto ComputeRAM, benchmark their performance and energy efficiency, and use those results to guide both customer engagements and product development. You will also work closely with Synthara's customers, supporting them in adding ComputeRAM® to their existing products.

This is a hands-on, cross-functional role. You will work with ML and AI models, as well as signal-processing and cryptography algorithms at the application level, but also reason about hardware constraints and tradeoffs, memory movement, numerical precision, system architecture, and performance. You will collaborate closely with software, applications, platform and hardware engineers, management and sales representatives, while also supporting customer-facing proofs-of-concept, workshops, pre-sales and technical discussions.

What you'll do

• Implement and benchmark AI, ML, and signal-processing applications on ComputeRAM-enhanced systems.
• Develop proofs-of-concept for prospective customers and partners, taking workloads from initial model analysis through implementation, benchmarking and presentation of results.
• Collaborate with hardware, architecture and software teams to identify missing modelling capabilities and product features, and feed those requirements into the technical roadmap.
• Produce clear technical documentation, application notes, benchmark reports, presentations and other technical collateral.

Outcomes (first 18 months)

• Maintain and expand company internal understanding of how ComputeRAM and software tools perform.
• Simulation tools for ComputeRAM systems are maintained and usable to explore new hardware design directions and generate easy to understand, concise reports.
• Developer and customer-quality documentation in place that makes benchmark assumptions, implementations and results easy to understand and reproduce.

Requirements

• Strong understanding of machine learning, deep learning and AI architectures, with practical experience using frameworks such as PyTorch or TensorFlow.
• Good understanding of SoC and computer architectures, or embedded SW
• Comfortable reading technical papers, model implementations, hardware documentation and specifications and translating them into working implementations.
• Clear technical writing and communication skills.
• Comfortable in dealing with ambiguity, working across disciplines and discussing technical requirements directly with customers, partners and internal engineering teams.
• Strong interpersonal skills and confidence engaging directly with customers, partners and cross-functional teams.
• Degree in computer science, electrical/electronic engineering, machine learning, applied mathematics, physics, engineering or a related quantitative field.

Nice to have (constitute a plus)

• Experience with embedded AI / TinyML or deploying neural networks onto resource-constrained devices.
• Understanding of neural-network accelerators, NPUs, GPUs, DSPs, systolic arrays or in-memory computing architectures.
• Experience with DSP workloads such as audio processing, filtering, FFTs, radar or communications algorithms.
• Experience with performance modelling, architectural simulation or hardware/software co-design.