Senior Applied Scientist / Engineer, Training & Inference
San Jose, California, United States · On-site
$164k–$313k/yr
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$164k–$313k/yr
Full-time
postgraduate degree
Annual Incentive Plan, New Hire Equity Award, Comprehensive Benefits Programs
Posted 17d ago
~40 hrs/week
Responsibilities
The role involves owning the end-to-end training-to-deployment pipeline for video and multimodal generative models. You will bridge the gap between research and production by optimizing distributed training and inference systems for performance and cost-efficiency.
Requirements
Candidates must have a Master's or PhD in a technical field and hands-on experience with large-scale distributed training using PyTorch. Proven expertise in deploying generative models to production and strong systems engineering skills are essential for this senior-level position.
Full job description
The Opportunity
Adobe Applied Science & Machine Learning (ASML) is seeking a Senior Applied Scientist / Engineer, Training & Inference to play a critical role in closing the gap between research and production for Adobe's next-generation video and image foundation models. In this role, you will serve as a technical owner for the training-to-deployment pipeline for our video and multimodal generation models. Rather than focusing solely on model research or systems infrastructure in isolation, you will bridge both — bringing the hands-on training expertise and the inference and deployment depth needed to take large generative models from the research cluster to reliable, performant, and cost-efficient production. This role is ideal for those who excels at the full arc of model development — distributed training at scale, inference optimization, and the practical engineering required to deploy and operate models reliably in production.
Job Responsibilities
Training & Inference Ownership. Own key components of the training-to-deployment pipeline — from distributed training execution through inference optimization, serving, and production handoff — ensuring models are delivered reliably, performantly, and cost-efficiently. Large-Scale Distributed Training. Implement and operate distributed training strategies including PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism across multi-node GPU environments, ensuring correctness, stability, and scalability for large video and multimodal models. Inference & Serving. Design and optimize inference and serving systems for large generative models, with a focus on latency, throughput, and cost across deployment targets. Research-to-Production Bridge. Reduce the gap between trained model checkpoints and reliable production deployments — owning the practical work of hardening, validating, and operationalizing models at scale. Performance & Cost-Aware Engineering. Identify and address inefficiencies across the training and inference stack — memory, communication, scheduling, and execution orchestration — with a clear focus on GPU efficiency and cost targets. Collaboration with Research & Engineering Teams. Partner closely with applied researchers, ML engineers, and infrastructure teams to align training and inference systems with model architecture needs and product delivery timelines.
What You'll Need to Succeed
Education: Master's or PhD in Computer Science, Electrical Engineering, AI/ML, or a related field, or equivalent practical experience.
Distributed Training Expertise: Hands-on experience with large-scale distributed training using PyTorch (FSDP, Tensor Parallelism, Pipeline Parallelism) across multi-node GPU environments.
Inference & Deployment Experience: Proven experience optimizing and deploying large generative models for production — including serving infrastructure, latency/throughput tuning, and cost-aware deployment.
Strong Systems & Engineering Skills: Proficiency in Python and PyTorch, with experience working in large shared codebases and contributing to production-critical ML systems.
Research-to-Production Execution: Demonstrated ability to take models from training through deployment, navigating the practical engineering challenges of reliability, reproducibility, and operational scale.
Senior-Level Ownership: Demonstrated ability to independently own end-to-end technical areas, drive cross-team execution, and deliver high-quality systems on which product teams depend.
Preferred Experience
Experience training and deploying video, image, or multimodal generative models (e.g., diffusion models, flow matching, video generation).
Familiarity with inference serving frameworks such as TensorRT, vLLM, or equivalent.
Experience with performance profiling and optimization for both training and inference workloads.
Track record of shipping generative AI models to production at scale.
Prior work in an applied research environment bridging ML and systems engineering.
About Adobe
Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.
Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.
Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.
Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com.
AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.
Expected Pay Range:
Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $164,000 -- $313,300 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.


In California, the pay range for this position is $216,400 - $313,300

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
State-Specific Notices:
California:
Fair Chance Ordinances
Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.
Colorado:
Application Window Notice
If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.
Massachusetts:
Massachusetts Legal Notice
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Adobe empowers everyone, everywhere to imagine, create, and bring any digital experience to life. From creators and students to small businesses, global enterprises, and nonprofit organizations — customers choose Adobe products to ideate, collaborate, be more productive, drive business growth, and build remarkable experiences.
Offices: 345 Park Avenue, San Jose, CA 95110-2704, US · 801 N 34th St, Seattle, WA 98103, US · 201 Sussex St, Sydney, NSW 2000, AU · 112 Avenue Kleber, Paris, IdF 75116, FR · Georg-Brauchle-Ring 58, Munich, BY 80992, DE
How much do Science & Research jobs in San Jose, CA pay?
Based on 678 listings with disclosed salaries, most science & research jobs in San Jose, CA pay between $100k–$260k per year. Individual offers vary with seniority, company size, and specialization.
How many Science & Research jobs are open in San Jose, CA right now?
There are currently 820 open science & research positions in San Jose, CA listed on Clera. New openings are added daily as companies post roles.
Which companies are hiring for Science & Research roles in San Jose, CA?
Companies currently hiring include Muon Space, Capital One, Hark Labs, Archer, Research and Innovation at San Jose State University, among others. Browse the listings above to see every active employer.
Are there remote or hybrid Science & Research jobs in San Jose, CA?
Yes — 176 of the 820 open science & research positions offer remote or hybrid work (31 remote, 145 hybrid).
How do I apply for Science & Research jobs in San Jose, CA?
Each listing links directly to the employer's application page. Apply early — fresh listings get the most recruiter attention in the first two weeks.