Level

CiscoPosted 1w ago

Machine Learning Engineering Technical Leader

Machine Learning Engineering Technical Leader at Cisco scores 97 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Remote (Seattle, Washington, US)seniorFull time$234k-$297k

AI in this role

llamapytorchtensorflow
fine-tuningml-opsai-research

Meet the Team
Splunk, a Cisco company, is building a safer, more resilient digital world with an end-to-end, full-stack platform designed for hybrid, multi-cloud environments. Join the Code Generation group, where we work on automating the code generation process using GenAI techniques. We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco’s global engineering capabilities. Our work spans networking, security, observability, and customer experience, designing and deploying foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform at enterprise scale. You’ll be part of a culture that values technical excellence, innovation, and collaboration, all within a flexible environment.

Your Impact
As a Senior Staff Applied Scientist, you will:

  • Own the full lifecycle of research and deployment of next-generation AI systems for intelligent code generation, including model design, evaluation, and production rollout.

  • Define the scientific roadmap for agentic GenAI, enabling models that not only generate code but reason, plan, self-correct, and integrate with tools and runtime environments.

  • Advance the state of the art in DSL-aware code synthesis, shaping how future developer experiences are powered by LLMs across interactive and automated workflows.

  • Drive efficiency and scalability of distributed training and inference pipelines to balance performance, latency, and cost — without compromising accuracy or reliability.

  • Collaborate closely with engineering and product to ensure AI breakthroughs translate quickly and safely into high-impact customer capabilities.

  • Mentor and elevate a high-performing research organization, fostering a culture of scientific rigor, creativity, and delivery excellence.

  • Shape long-term AI strategy and innovation, influencing architectural decisions, technical investments, and roadmap direction across the GenAI organization.

Minimum Qualifications

  • PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field and 5+ years of post-doctoral or industry experience; or a Master’s degree in a related field and 10+ years of industry experience.

  • 3+ years experience in developing Large Language Models (LLMs) for program synthesis or formal languages

  • 2+ years experience in Multi-step planning or agentic AI for developer workflows

  • 3+ years experience with Python and deep learning frameworks such as PyTorch or TensorFlow.

  • 3+ years experience translating research prototypes into production systems, including model deployment, optimization, and evaluation.


Preferred Qualifications

  • Strong foundation in experimental design, benchmarking, reproducibility, evaluation metrics, and scientific documentation.

  • LLMs for Code Generation — Experience with training, fine-tuning, or adapting models such as Code-LLaMA, CodeT5, StarCoder, or GPT-based code models for program synthesis, refactoring, unit test generation, static/dynamic analysis, or domain-specific languages (DSLs).

  • Domain-Specialized Modeling — Background building generative models that target structured languages (e.g., SQL, DSLs, configuration languages, or proprietary query languages/SPL).

  • Agentic AI & Tool Use — Experience designing agents that plan, call tools/APIs, self-reflect, or drive code to iteratively refine solutions.

  • Structured Reasoning & Planning — Proven success applying techniques such as chain-of-thought, self-debugging, constrained decoding, or reinforcement learning for software development tasks.

  • Large-Scale Training & Optimization — Experience with distributed training, efficient inference (quantization, LoRA, caching, batching), and cost-aware scaling.

  • MLOps & Continuous Evaluation — Familiarity with automated model retraining, dataset curation, synthetic data pipelines, eval harnesses, and model health monitoring.

  • Research Leadership — Publications in premier AI/ML venues (NeurIPS, ICML, ICLR, ACL, AAAI, KDD, etc.) and/or recognized contributions in the code-gen / LLM community.

Why Cisco? 

At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.

Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. 

We are Cisco, and our power starts with you. 

Message to applicants applying to work in the U.S. and/or Canada:

The starting salary range posted for this position is $234,400.00 to $296,600.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.

Individual pay is determined by the candidate's hiring location, market conditions, job-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process.

U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks.  Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time.

U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies:

  • 10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees

  • 1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco

  • Non-exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full-time employees

  • Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations)

  • 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next

  • Additional paid time away may be requested to deal with critical or emergency issues for family members

  • Optional 10 paid days per full calendar year to volunteer

For non-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies.

Employees on sales plans earn performance-based incentive pay on top of their base salary, which is split between quota and non-quota components, subject to the applicable Cisco plan. For quota-based incentive pay, Cisco typically pays as follows:

  • .75% of incentive target for each 1% of revenue attainment up to 50% of quota;

  • 1.5% of incentive target for each 1% of attainment between 50% and 75%;

  • 1% of incentive target for each 1% of attainment between 75% and 100%; and

  • Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.

For non-quota-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid.

The applicable full salary ranges for this position, by specific state, are listed below:

New York City Metro Area:

$256,900.00 - $383,500.00

Non-Metro New York state & Washington state:

$234,400.00 - $341,100.00

* For quota-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.

** Employees in Illinois, whether exempt or non-exempt, will participate in a unique time off program to meet local requirements.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningMl OpsAI ResearchLlamaPyTorchTensorFlow

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. Tell me about a research question you investigated. What did you find?
  4. What's a project where you used Llama hands-on?
  5. Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Fine Tuning, Ml Ops, AI Research, Llama, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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