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About This Role
About Tensordyne
Tensordyne is building a new class of AI inference system designed for high-performance, power-efficient deployment of the world's most demanding generative AI workloads.
Our platform combines purpose-built silicon, new AI math, optimized scale-up networking, and memory architecture into a tightly integrated system purpose built for large-scale AI inference. We work with hyperscalers, neoclouds, frontier model developers, enterprises, and infrastructure partners operating at the leading edge of AI.
As Tensordyne moves from system development into silicon bring-up, customer validation, beta deployments, and production rollout, we are building the technical customer organization that will sit directly between our engineering teams and the companies deploying the platform.
We are looking for an exceptional technical leader to help build and lead that function.
The Role
Tensordyne is hiring a Director of AI Systems Solutions Engineering to own and grow our most important technical customer engagements.
This is a senior, highly technical role for someone who understands modern AI infrastructure from model architecture through accelerator hardware, distributed inference, serving software, and datacenter deployment — and who can credibly engage with the engineers and architects building the next generation of AI platforms.
You will work directly with frontier model builders, hyperscalers, neoclouds, developers, infrastructure partners, and strategic customers as they evaluate and deploy Tensordyne systems.
You will also build and lead a small team of exceptional Sales and Solutions Engineers responsible for customer benchmarking, technical evaluation, NPI, model enablement, AI DC architecture, and production deployment.
This is not a traditional pre-sales engineering role. The team will operate at the frontier of a rapidly changing technology landscape, working with constantly evolving new models and requirements. The right person will be equally comfortable in a customer architecture review, helping prioritize product capabilities and roadmaps, and leading a technical evaluation with hyperscalers and frontier AI companies.
What You Will Own
- Strategic technical customer engagements: Own the technical relationship with key customers and partners from initial architecture discussions through benchmarking, evaluation, integration, deployment, and expansion.
- Technical evaluation strategy: Define how Tensordyne demonstrates system performance across KPI's like throughput, tokens/sec/user, ttft, memory utilization, power efficiency, system density, model accuracy/quality, and other relevant inference metrics.
- AI workload and model architecture engagement: Work with customers and model developers to understand current and emerging HW and model architectures, serving requirements, context lengths, parallelism strategies, model topology, quantization approaches, and inference optimization requirements.
- Benchmarking and competitive analysis: Maintain a technically rigorous understanding of Tensordyne performance relative to leading GPU and AI accelerator platforms. Ensure customer-facing comparisons are credible, reproducible, current, and aligned with real deployment requirements.
- Model enablement and optimization: Partner with compiler, runtime, kernel, systems, and SDK teams to bring important customer models and workloads onto the Tensordyne platform and identify opportunities for performance improvement.
- Forward deployment: Lead technical PoCs, remote evaluations, on-premises beta deployments, integration programs, and production readiness efforts with strategic customers.
- Customer-to-product feedback loop: Translate recurring customer requirements into clear priorities for the SDK, compiler, runtime, inference server, model support, orchestration, networking, observability, and system architecture teams.
- Technical market intelligence: Stay deeply current on model architectures, inference techniques, accelerator roadmaps, serving frameworks, competitive systems, benchmarking methodologies, and changes in the AI infrastructure market.
- Team leadership: Help recruit, develop, and lead a small team of elite Sales/Solutions Engineers capable of independently managing sophisticated technical engagements with the world's most demanding AI infrastructure customers.
- Scalable technical GTM: Turn early customer engagements into repeatable benchmarks, evaluation frameworks, reference architectures, deployment playbooks, documentation, demos, and technical collateral that can support a rapidly growing customer base.
What We Are Looking For
We are looking for a proven AI systems leader with substantial technical depth and strong judgment.
You should bring:
- Deep understanding of modern AI inference systems, including LLM and multimodal architectures.
- Strong knowledge of AI accelerator and system architecture, including compute, memory hierarchy, interconnect, parallelism, and distributed inference.
- Experience reasoning about inference performance across latency, throughput, memory bandwidth, utilization, batching, context length, prefill, decode, and system scaling.
- Hands-on familiarity with modern AI frameworks and serving environments such as PyTorch, vLLM, SGLang, Triton, or comparable systems.
- Experience working across the boundary between AI software and accelerator hardware, ideally including GPUs, custom silicon, or emerging AI accelerators.
- Experience benchmarking and optimizing workloads on large-scale AI infrastructure.
- Strong understanding of production inference techniques including quantization, tensor/model/expert parallelism, disaggregated serving, KV-cache management, distributed execution, and related optimization strategies.
- Demonstrated ability to engage technically sophisticated external organizations including Sr technical leaders at hyperscalers, cloud providers, model developers, AI infrastructure companies, or large enterprise engineering teams.
- Experience leading high-performing Solutions Engineering, Field Engineering, Forward Deployed Engineering, or comparable technical customer teams.
- Ability to operate effectively in a fast-moving environment where the product, software stack, competitive landscape, and customer requirements are evolving simultaneously.
Particularly Relevant Experience
Candidates may come from organizations building or deploying:
- GPU or custom AI accelerator platforms
- Large-scale AI inference infrastructure
- Frontier or foundation models
- Hyperscale cloud infrastructure
- AI serving and orchestration platforms
- Compiler, runtime, or distributed AI systems
- High-performance computing or distributed systems
Experience bringing a new accelerator architecture or AI infrastructure platform from early access through customer validation and production deployment is especially valuable.
Why This Role Matters
Tensordyne is entering the stage where our technology moves from internal development into the hands of customers.
The technical customer organization will play a central role in that transition.
This team will help determine which workloads we prioritize, how customers evaluate our platform, how quickly new models become production-ready, how effectively customer feedback reaches engineering, and ultimately how Tensordyne systems are adopted at scale.
We are looking for someone who wants to build that capability from the beginning — and establish the technical standard for how Tensordyne engages with the companies defining the future of AI infrastructure.
Tensordyne Values
- Think big. Pursue ambitious technical and business goals.
- Aim for excellence. Quality matters in everything we build and deliver.
- Own it and get it done. Take responsibility and drive results.
- Operate with integrity. Be direct, transparent, and respectful.
- Win as a team. Make the people around you more effective.
Tensordyne is an equal opportunity employer. We believe that a diverse team is better at tackling complex problems and coming up with innovative solutions. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
A note to Recruitment Agencies: Please don't reach out to Tensordyne employees or leaders about our roles - we've got it covered. We don't accept unsolicited agency resumes and we are not responsible for any fees related to unsolicited resumes. Thank you for your understanding.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Tensordyne, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Tensordyne AI Hiring
Tensordyne has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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