Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security.
Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive. Visit us at Netskope Careers. Please follow us on LinkedIn and Twitter@Netskope.
Positions are available at Senior Staff and above. Candidates are assessed individually and leveled according to their specific skills and background.
About the roleAs a Senior Staff Machine Learning Scientist, you own the inference and optimization layer that makes AI in agentic workflows fast, efficient, and production-grade. You fine-tune and evaluate models, push latency and throughput on real hardware, and build the runtime that executes bounded AI tasks, validated against usage from Netskope’s large customer base so you optimize where the data points, not where you guess.
What’s in it for you- High-impact ownership. You own the model layer of a net-new product that changes the performance and economics of agentic AI.
- Cutting-edge, unusual stack. The hard, interesting inference problems live here: quantization, KV-cache and memory management, sparsity, fine-tuning, and hardware acceleration under real-world resource constraints.
- Real scale to build against. Netskope’s customer footprint gives you production signals most teams never see, so you deploy, validate, and iterate fast.
- Build and optimize the model inference path: quantization, KV-cache optimization, batching, and latency/memory/throughput tuning on constrained, commodity hardware.
- Fine-tune and evaluate models for bounded tasks; build eval harnesses that gate a capability to release on real accuracy, latency, and security relevance.
- Design and grow the task execution runtime (bounded sub-agents), pushing toward dynamic task generation and context compaction.
- Drive hardware acceleration / sparsity and support for larger models as the platform matures.
- Partner with the systems and backend engineers to ship capabilities end-to-end and iterate on real production signals.
- 10+ years of overall industry experience, with 4+ years hands-on in ML/AI (model development, fine-tuning, and inference optimization).
- Hands-on with fine-tuning (e.g. LoRA/QLoRA), quantization (GGUF/AWQ/GPTQ), and inference runtimes (vLLM/SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, or MLX/CoreML). On-device or edge inference experience is a strong plus.
- Strong Python; comfort reaching into C++ for low-level interop is a plus.
- Solid grasp of transformer internals and the levers that move real inference performance and cost: KV cache, attention, batching, memory footprint.
- Fluency with agentic coding systems and genuine curiosity about agent harnesses like Claude Code, Pi, and Codex, so you should already be building with them, or itching to.
- Clear communication: able to distill a model or infra bottleneck into an actionable concept for cross-functional teammates.
- MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree required, with a focus in AI/ML research; PhD in a related field strongly preferred.
Compensation:
At Netskope, salary is one component of our competitive total rewards package. The salary range for this position is as listed below. This is a national range. For purposes of complying with applicable laws, the range applies to candidates in California, Colorado, Illinois, Maryland, New York, Washington, and other states.
The successful candidate’s starting pay will also be determined based on job-related skills, experience, qualifications, location, and market conditions.
For all sales roles, the posted salary range is the On Target Earnings (OTE) range for the role, which is the sum of base salary and target commission amount at 100% goal achievement.
In addition to salary, candidates may be eligible for other forms of compensation such as participation in a bonus plan (for non-sales roles) and a stock award program. Candidates may also be eligible for a comprehensive health plan and other benefits that can be reviewed at Netskope Benefits site.
Netskope is committed to implementing equal employment opportunities for all employees and applicants for employment. Netskope does not discriminate in employment opportunities or practices based on religion, race, color, sex, marital or veteran statues, age, national origin, ancestry, physical or mental disability, medical condition, sexual orientation, gender identity/expression, genetic information, pregnancy (including childbirth, lactation and related medical conditions), or any other characteristic protected by the laws or regulations of any jurisdiction in which we operate.
Netskope respects your privacy and is committed to protecting the personal information you share with us, please refer to Netskope's Privacy Policy for more details.
The application window for this position is expected to close within 50 days. You may apply by filling out the below information, or visiting our Netskope Careers site.
Skills Required
- 10+ years overall industry experience with 4+ years hands-on in ML/AI (model development, fine-tuning, inference optimization)
- MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree
- PhD in a related field
- Hands-on experience with fine-tuning techniques (e.g., LoRA, QLoRA)
- Experience with quantization methods (GGUF, AWQ, GPTQ)
- Experience with inference runtimes (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, MLX/CoreML)
- Strong Python programming skills
- Comfort reaching into C++ for low-level interop
- Solid grasp of transformer internals and inference levers (KV cache, attention, batching, memory footprint)
- Fluency with agentic coding systems and agent harnesses (e.g., Claude Code, Pi, Codex)
- Clear communication skills to explain model/infra bottlenecks across cross-functional teams
- On-device or edge inference experience
Netskope Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Netskope and has not been reviewed or approved by Netskope.
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Fair & Transparent Compensation — Pay is considered competitive in U.S. technical roles, with indications that total compensation benchmarks strongly versus peers. Employees are generally pleased with total compensation that includes pay, stock, equity, and benefits.
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Healthcare Strength — Health coverage offers multiple medical plan choices alongside dental, vision, disability, HSA/FSA, and EAP, and is often characterized as very good. Employer-verified plan options and regional choices reinforce the breadth and quality of coverage.
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Leave & Time Off Breadth — Time off includes flexible/unlimited PTO, paid holidays, and quarterly wellness days that support rest and flexibility. Paid parental leave is available, commonly cited at 12 weeks for birth parents and 8 weeks for non‑birth parents.
Netskope Insights
What We Do
Netskope, the SASE leader, safely and quickly connects users directly to the internet, any application, and their infrastructure from any device, on or off the network. With CASB, SWG, and ZTNA built natively in a single platform, the Netskope Security Cloud provides the most granular context, via patented technology, to enable conditional access and user awareness while enforcing zero trust principles across data protection and threat prevention everywhere. Unlike others who force tradeoffs between security and networking, Netskope’s global security private cloud provides full compute capabilities at the edge. Netskope is fast everywhere, data-centric, and cloud-smart, all while enabling good digital citizenship and providing a lower total-cost-of-ownership.
