Tenstorrent’s AI Software Infrastructure team builds the platforms that power internal development, orchestrate workloads, and manage large-scale AI hardware across on-prem data centers. This team develops and productionizes infrastructure used both internally and externally on Tenstorrent systems.

This role is hybrid, based out of Toronto, ON; Santa Clara, CA; Austin, TX; Belgrade, Serbia; Warsaw, Poland; or Gdańsk, Poland.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• Strong backend or infrastructure engineer with experience building and operating platforms on bare-metal or on-prem systems.
• Deep experience with Kubernetes, including cluster provisioning, operations, and debugging production issues.
• Proficient in Python or Go with experience building APIs and services.
• Comfortable with Linux systems, networking fundamentals, and debugging distributed systems.
• Collaborative and adaptable, able to work across teams in fast-moving environments.

What We Need

• Design, build, and maintain infrastructure platforms for development workflows, workload orchestration, and ML services.
• Develop APIs and services that enable internal teams to interact with infrastructure systems.
• Productionize and scale Kubernetes-based platforms, including cluster management and operational maturity.
• Integrate automation workflows using CI/CD pipelines and infrastructure-as-code tools such as Ansible and GitOps frameworks.
• Collaborate with SRE, infrastructure, and deployment teams to support large-scale on-prem and customer-facing environments.

What You Will Learn

• How large-scale AI infrastructure is built and operated on custom accelerator hardware.
• Advanced Kubernetes patterns including operators, controllers, and production-scale cluster management.
• How on-prem data center infrastructure differs from cloud-native environments at scale.
• How infrastructure platforms evolve from internal tools to production-grade external systems.
• How cross-functional teams coordinate to deploy and support AI systems in real-world environments.

Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

Tenstorrent is building next-generation AI compute. The Kernel Development and Optimization team develops the performance-critical kernels that unlock the full capability of our hardware across ML and HPC workloads.

This role is hybrid based out of Warsaw or Gdansk, Poland.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• A strong C++ systems engineer with experience writing performance-critical or low-level software.
• Comfortable reasoning about concurrency, synchronization, latency hiding, and compute versus memory trade-offs.
• Data-driven in your approach, using profiling and benchmarking results to guide optimization decisions.
• Effective at debugging complex runtime or kernel-level issues in large codebases.
• Structured thinker who can break down ambiguous performance problems into measurable experiments.

What We Need

• Engineers who can design, implement, and optimize GPU-style kernels such as matrix multiplication, attention primitives, and data-movement operations.
• Clear ownership of performance, from identifying bottlenecks to delivering measurable throughput improvements.
• Contribution to host-side orchestration code and parallelization strategies.
• Development of micro-benchmarks, regression tests, and tooling to ensure correctness and sustained performance gains.
• Close collaboration with compiler, runtime, ML, and hardware teams to integrate kernels into production systems.

What You Will Learn

• The execution model, memory architecture, and performance characteristics of Tenstorrent AI hardware.
• How to write and optimize accelerator kernels outside traditional CUDA-first ecosystems.
• Practical AI-assisted and agentic workflows for kernel generation, debugging, and optimization.
• How to translate performance intuition into rigorous, reproducible engineering results.
• How low-level kernels, compilers, runtime systems, and hardware co-evolve in modern AI platforms.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

As a C++ Machine Learning Engineer on the AI Models team at Tenstorrent, you’ll work on the training framework behind our most advanced models. You’ll write high-performance C++ code, shape how new layers and operators are implemented, and help models scale across our custom silicon. If you enjoy building the guts of ML systems and seeing them run fast, this role is for you.

This role is hybrid, based out of Warsaw or Gdansk, Poland. We also consider remote candidates on a case-by-case status.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• Strong in C++ and low-level systems programming, especially in performance-critical code.
• Comfortable thinking in tensors, memory layout, and compiler graphs.
• Familiar with PyTorch and curious about how frameworks map to hardware.
• A builder who enjoys solving technical puzzles and digging into the details.

What We Need

• Extend and optimize our ML training framework with new ops, layers, and training features.
• Debug and tune model performance on Tenstorrent chips.
• Work with compiler and kernel teams to make sure models compile and run as expected.
• Support integration of real-world models and help bring them into production.

What You Will Learn

• How ML frameworks and compilers connect at the system level.
• How to translate training workloads into low-level operations optimized for custom silicon.
• How large-scale model training works under the hood, from memory layout to operator fusion.
• What it takes to build infrastructure that supports fast iteration in research and production.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.