ml systems engineer

The Home Depot

<div><div>Position Purpose:
The Staff Software Engineer is responsible for leading a team of engineers building and designing a product that our customers and associates love. As a Staff Software Engineer, you will be part of a dynamic team with engineers of all experience levels who help each other build and grow technical and leadership skills while creating, deploying, and supporting production applications. In this role, you will also provide technical leadership on machine learning systems, including model development, production deployment, monitoring, and lifecycle management of ML solutions operating at scale. In addition, Staff Software Engineers will assist in product and tool selection, configuration, security, resilience, performance tuning and production monitoring. 
Staff Software Engineers contribute to foundational code elements that can be reused as well as architectural diagrams and other product-related documentation. You will help define best practices for building reliable, explainable, and maintainable ML systems that integrate seamlessly with broader software platforms. 
As a Staff Software Engineer, you will be a core player on the product team and are expected to build and grow the skillsets of the more junior Engineers. 
Key Responsibilities:<ul><li>
45% Delivery and Execution - Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions, Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively</li><li>
15% Learning - Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations; Attends conferences and learns how to apply new innovations and technologies where appropriate</li><li>
20% Strategy and Planning - Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives; Leads the evaluation development and recommendation of specific technology products and platforms to provide cost-effective solutions that meet business and technology requirements; Researches and designs best fit infrastructure, network, database, security, and machine learning architectures for products; Proactively creates and maintains tools for monitoring and support; Participates in project planning and management across multiple efforts; Develops formal training courses</li><li>
20% Support and Enablement - Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality</li></ul> 
Direct Manager/Direct Reports:<ul><li>
This Position typically reports to Software Engineer Manager or Sr Software Engineer Manager</li><li>
This Position has 0 Direct Reports</li></ul> 
Travel Requirements:<ul><li>
Typically requires overnight travel 5% to 20% of the time.</li></ul> 
Physical Requirements:<ul><li>
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.</li></ul> 
Working Conditions:<ul><li>
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.</li></ul> 
Minimum Qualifications:<ul><li>
Must be eighteen years of age or older.</li><li>
Must be legally permitted to work in the United States.</li></ul> 
Preferred Qualifications:<ul><li>
3 - 6 years of relevant work experience</li><li>
Strong experience designing, training, evaluating, and deploying machine learning models in production environments, including batch and real-time inference systems</li><li>
Experience with ML lifecycle management, including feature engineering, model versioning, experimentation, validation, and monitoring for data drift and model performance degradation</li><li>
Experience building and operating ML pipelines using cloud-native services, data platforms, and CI/CD practices for reproducible and reliable model deployment</li><li>
Strong understanding of applied statistics, model evaluation metrics, and tradeoffs between model accuracy, interpretability, latency, and operational cost</li><li>
Experience with algorithms such as clustering, forecasting, anomaly detection, and neural networks.</li><li>
Experience to basic statistics and regression algorithms</li><li>
Experience in advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization</li><li>
Experience in training machine learning models with extremely large datasets</li><li>
Experience with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, tensorflow, pytorch, etc. - and experience integrating them into scalable software systems</li><li>
Experience in Google Cloud Platform and AI/ML related components such as Vertex AI, BigQueryML, and AutoML</li><li>
Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc</li><li>
Experience in a modern scripting language (preferably Python)</li><li>
Experience in writing SQL queries against a relational database</li><li>
Experience in version control systems (preferable Git)</li><li>
Experience in a Linux or Unix based environment</li><li>
Experience in a CI/CD toolchain</li><li>
Experience in REST and effective web service design</li><li>
Experience in production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security</li><li>
Experience in cloud computing platform and associated automation patterns and machine learning services they provide</li><li>
Experience in defensive coding practices and patterns for high Availability</li><li>
Experience in A/B testing and effective REST design for scalable web services architecture</li><li>
Familiarity with advanced machine learning architectures GANs, GRU, LSTMs, RNNs, CNNs, style transfer</li></ul> 
Minimum Education:<ul><li>
The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.</li></ul> 
Preferred Education:<ul><li>
No additional education</li></ul> 
Minimum Years of Work Experience:<ul><li>
3</li></ul> 
Preferred Years of Work Experience:<ul><li>
No additional years of experience</li></ul> 
Minimum Leadership Experience:<ul><li>
None</li></ul> 
Preferred Leadership Experience:<ul><li>
None</li></ul> 
Certifications:<ul><li>
None</li></ul> 
Competencies:<ul><li>
Global Perspective</li><li>
Manages Ambiguity</li><li>
Nimble Learning</li><li>
Self-Development</li><li>
Collaborates</li><li>
Cultivates Innovation</li><li>
Situational Adaptability</li><li>
Communicates Effectively</li><li>
Drives Results</li><li>
Interpersonal Savvy</li></ul></div> 
Apply End Date: 04/01/2026</div>

Staff Machine Learning Engineer

Call For Referral

<div>
 
 
 Machine Learning Engineer
 
 
 
 
 Work Mode:
 
 
 Remote
 
 
 
 
 Compensation:
 
 
 $100&ndash;$120 per hour
 
 
 
 
 Work Type:
 
 
 Hourly, Independent
 
 
 
 
 Role Overview
 
 
 
 This role supports the evaluation of
 
 advanced machine learning systems
 
 in collaboration with
 
 leading AI research initiatives
 
 . It is intended for
 
 experienced machine learning engineers and researchers
 
 who can contribute to the development of
 
 high-quality evaluation suites
 
 that measure AI performance on
 
 real-world machine learning engineering tasks
 
 .
 
 
 The work focuses on translating
 
 practical ML research and engineering workflows
 
 into
 
 structured benchmarks
 
 for
 
 frontier models
 
 . This is a
 
 project-based, fully remote
 
 opportunity for professionals with
 
 strong hands-on ML research and engineering experience
 
 .
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 
 Design and develop
 
 detailed evaluation suites for machine learning engineering tasks
 </li>
 <li>
 
 Evaluate AI-generated solutions
 
 across model training, debugging, optimization, and experimentation workflows
 </li>
 <li>
 
 Analyze and document
 
 system behavior, performance characteristics, and limitations
 </li>
 <li>
 
 Contribute to the creation of benchmarks
 
 that reflect real-world ML engineering practices
 </li>
 <li>
 
 Maintain high standards
 
 of technical accuracy, clarity, and consistency in all deliverables
 </li>
 </ul>
 
 
 Qualifications
 
 
 <ul>
 <li>
 
 3+ years
 
 of experience in machine learning engineering or applied ML research
 </li>
 <li>
 
 Hands-on experience
 
 with model development, experimentation, and evaluation
 </li>
 <li>
 
 Background in ML research
 
 in an industry or academic setting (strongly preferred)
 </li>
 <li>
 
 Strong ability
 
 to reason about ML system design choices and technical trade-offs
 </li>
 <li>
 
 Excellent written communication skills
 
 and
 
 high attention to technical detail
 
 </li>
 </ul>
 
 
 Work &amp; Payment Details
 
 
 <ul>
 <li>
 You will operate as an
 
 independent professional
 
 </li>
 <li>
 This is a
 
 fully remote
 
 role with a
 
 flexible schedule
 
 </li>
 <li>
 Projects may be
 
 extended, shortened, or concluded
 
 based on needs and performance
 </li>
 <li>
 The work does
 
 not involve access to confidential or proprietary information
 
 from any employer, client, or institution
 </li>
 <li>
 
 Payments are made weekly
 
 via
 
 Stripe or Wise
 
 , based on services delivered
 </li>
 <li>
 Candidates requiring
 
 H1-B or STEM OPT
 
 sponsorship are
 
 not supported
 
 at this time
 </li>
 </ul>
 
 
 Application &amp; Onboarding Process
 
 
 <ul>
 <li>
 
 Upload your resume
 
 </li>
 <li>
 
 AI interview:
 
 A short
 
 15-minute
 
 conversational session to understand your background, experience, and interest in the role
 </li>
 <li>
 
 Follow-up communication
 
 within a few days with next steps and onboarding details
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer (Remote)  $100 –$120/hr

Innodata

<div>Who we are:
Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world&rsquo;s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.
By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we&rsquo;re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We&rsquo;re poised for a period of explosive growth over the next few years.
Position Summary:
Innodata is expanding its team of technical experts in LLM training, post-training, and evaluation systems. As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will build and optimize the technical foundations that power model improvement for foundation model builders and leading labs.
This role is ideal for someone who has hands-on experience fine-tuning and evaluating large language models (and ideally multimodal models), and who can bridge research and engineering in real-world customer environments. You will work closely with Language Data Scientists, Applied Research Scientists, data engineers, and client technical stakeholders to design and implement robust training/evaluation pipelines using both human-in-the-loop and AI-augmented methods.
The ideal candidate brings a strong computer science / machine learning engineering background, experience with modern LLM post-training workflows, and the ability to engage credibly with technical counterparts at leading AI organizations.
Who We&rsquo;re Looking For:
You have at least 2-3 years of relevant experience in machine learning engineering, applied ML systems, or research engineering, with substantial hands-on work in LLMs and multimodal foundation models. You have built, adapted, or optimized model training and evaluation pipelines, and you understand the practical realities of experimentation at scale: reproducibility, debugging, metrics quality, and iteration speed.
You are comfortable operating in ambiguous, high-complexity environments and can move from problem framing to implementation. You can collaborate effectively with both researchers and engineers, and you are credible in technical conversations with sophisticated customer stakeholders (e.g., AI researchers, ML engineers, technical product leads).
You bring a builder mindset and strong engineering judgment, while also understanding that evaluation quality and data quality are central to model improvement. You are excited to partner with human evaluation experts and language data scientists to create integrated post-training and evaluation systems.
Tell Me More:
As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will design and implement the pipelines and tooling that connect data, evaluation, and post-training. You will help customers and internal teams move from evaluation findings to measurable model improvements.
Your work may include building fine-tuning workflows (e.g., supervised fine-tuning and preference-based optimization), integrating evaluation harnesses into model development loops, improving experiment reliability and throughput, and supporting advanced evaluation scenarios such as long-context, cross-modal, and dynamic multi-turn interactions.
You will also contribute to Innodata&rsquo;s internal R&amp;D efforts, including benchmark datasets, evaluation frameworks, and reusable infrastructure for model assessment and post-training experimentation.
Responsibilities:<ul><li>
Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery</li><li>
Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking</li><li>
Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses</li><li>
Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows</li><li>
Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring</li><li>
Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift</li><li>
Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems</li><li>
Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions</li><li>
Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements</li><li>
Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects</li><li>
Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team</li></ul><ul><li>
BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred)</li><li>
2-3 years of relevant industry or research engineering experience in ML/AI systems</li><li>
Hands-on experience with LLM training / fine-tuning / post-training, including at least one of:<ul><li>
supervised fine-tuning (SFT)</li><li>
preference optimization (e.g., DPO or related methods)</li><li>
RLHF / RLAIF-style workflows</li><li>
task- or domain-adaptation of foundation models</li></ul></li><li>
Strong programming skills in Python and experience building production-quality ML code</li><li>
Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)</li><li>
Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons</li><li>
Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging</li><li>
Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred)</li><li>
Experience with large-scale data processing and workflow orchestration in support of model training/evaluation</li><li>
Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads</li><li>
Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences</li></ul>
Technical Skills
ML / LLM Engineering<ul><li>
Experience training, fine-tuning, and evaluating transformer-based models</li><li>
Understanding of post-training workflows and model iteration loops</li><li>
Familiarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deployment</li></ul>
Evaluation &amp; Experimentation<ul><li>
Experience implementing automated evaluation pipelines and test harnesses</li><li>
Experience with experiment tracking, versioning, and reproducibility practices</li><li>
Ability to assess metric quality and ensure consistency across model comparisons</li></ul>
Software / Data Engineering<ul><li>
Proficiency in Python and strong software engineering fundamentals</li><li>
Experience with data processing pipelines, storage formats, and scalable dataset workflows</li><li>
Familiarity with CI/CD, testing, and engineering quality practices for ML systems</li></ul>
Preferred Skills<ul><li>
Experience with multimodal model training/evaluation (text + image/audio/video)</li><li>
Experience with long-context evaluation and/or model adaptation for long-context tasks</li><li>
Experience with agentic or multi-turn evaluation harnesses, tool-use simulation, or interactive environment testing</li><li>
Experience working in customer-facing technical consulting, solutions engineering, or applied research delivery</li><li>
Familiarity with LLM safety, alignment, robustness, or red-teaming evaluation approaches</li><li>
Contributions to open-source ML/LLM tooling or published technical work in relevant areas</li></ul>
How this role partners with the team
This role works closely with:<ul><li>
Language Data Scientists, who lead human evaluation design, language/data process excellence, and annotation/synthetic workflows</li><li>
Applied Research Scientists, who lead evaluation methodology, benchmarking research, and experimental design</li><li>
Data Engineers / Platform Teams, who support scalable data and infrastructure foundations</li><li>
Customer Technical Stakeholders, who rely on Innodata for expert guidance and implementation support in advanced GenAI development</li></ul>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission&rsquo;s guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you&rsquo;ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.
#LI-NS1</div>

AI/ML Research Engineer, LLM Post-Training & Evaluation

Framatome North America

<div>Why This Role Is Critical:
<div>
As the need for nuclear energy grows, the future couldn&rsquo;t be brighter. Join our vital mission to create lasting solutions for our planet&rsquo;s greatest challenges. In this role, you are ready to shape the future of clean energy and make a long-lasting global impact. With us, anticipate engaging and challenging projects that deliver innovative solutions and value-added technologies helping power more than 38 million North American homes.</div><div></div><div> 
You will work as an ML Systems Engineer building software for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current). You will design the data preprocessing steps and ML model architecture to support the core function of the software. You&rsquo;ll collaborate with subject-matter experts to understand the trade-offs and specificities of the NDE workflows, adapt the architecture to them, validate the results, and ensure reproducibility of the results. Early projects will have well-defined scopes focused on technical execution, with more flexibility and responsibility for shaping deployment patterns as the team grows.</div>
What You&rsquo;ll Do Day-To-Day:
<ul><li>Works from detailed programming specifications, designs, modifies, develops, writes and implements software-programming applications.</li><li>
Participates in the testing process through test review and analysis, test witnessing and certification of software.</li><li>
Supports and/or installs software applications/operating systems.</li><li>
Manages installation and configuration of more complex third-party applications.</li><li>
Provides resolution to a fundamental problem or seizes an opportunity that improves the satisfaction of a customer, user or provider.</li><li>
Explains the financial impact of a solution or a decision to assist customers in complying with budget requirements.</li><li>
May assume ownership of a specific application. Ownership = operational leadership, strategic leadership, responsible for creating and defending software design, technical leadership within a task or project that changes the software, interface with IT, vendor/consultant/subcontractor/customer management.</li><li>
May provide guidance to less experienced Software Engineers.</li><li>
Will work as an ML systems engineer building software for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current).</li><li>
Design the data preprocessing steps and ML model architecture to support the core function of the software.
</li><li>Collaborate with subject-matter experts to understand the trade-offs and specificities of the NDE workflows, adapt the architecture to them, validate the results, and ensure reproducibility of the results.
</li></ul>What You'll Bring:
<ul><li>Bachelor&rsquo;s Degree in Computer Science, Engineering or a related field.</li><li>
At least 5 years of related experience is required, equivalent work experience may be considered in lieu of degree.</li><li>
At least 2 years in a hands-on role in a machine learning Engineering or Data Science context.</li><li>
Can develop high-quality work within a predefined problem scope, with emphasis on technical implementation, experimentation, and reproducibility.</li><li>
Exercises technical judgment by articulating non-negotiables and tradeoffs, for example, classical vs. deep learning, hardware-driven constraints, engineered vs. learned features, competing frameworks, libraries, or architectural decisions.</li><li>
Actively seeks to understand subject matter domain constraints and asks progressively better questions without being prompted to define success metrics for pilot projects.</li><li>
Assesses dataset readiness before starting model work, designs preprocessing steps, bootstraps custom model training or transfer-learning workflows, beyond notebooks, in modular Python code including data preprocessing (for visual- or audio-like data), experiment observability, and model versioning.</li><li>
Can embed an ML model in a modular inference workflow capable of integration with internal systems, instrumenting for basic observability and performance tracing in production or pre-production settings.</li></ul>
Total Rewards Package:
<ul><li>Salary: $85,000 - $116,000, and may also include annual incentives and performance bonuses. The base salary range is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location.</li><li>
Health &amp; Wellness: Multiple medical plan options, dental &amp; vision coverage, life insurance, long- and short-term disability, and optional supplemental plans (critical illness, accident, hospital indemnity, dependent life).</li><li>
Retirement: 401(k) with employer match.</li><li>
Paid Time Off: Up to 3 weeks of vacation, 8 sick days, and 13 paid holidays annually.</li><li>
Extra Perks: Lifestyle spending account, employer paid mental health support, education reimbursement, professional development opportunities, adoption assistance, parental leave, and employee assistance programs.</li></ul>
About Framatome:
<div>
Framatome has been shaping the future of nuclear energy in the U.S. for more than 60 years. Framatome Inc. is headquartered in Lynchburg, Virginia, with more than 2,400+ employees across North America supporting nearly every nuclear plant in the nation.</div><div></div><div> 
From our facilities in Virginia, Washington, North Carolina, Pennsylvania, Massachusetts and beyond, we deliver outage services, advanced nuclear fuel technologies, and next-gen reactor innovations that keep the lights on.</div><div></div><div> 
At Framatome, you will join a global team committed to creating reliable, carbon-free energy. Here, you can:</div><ul><li>
Solve complex nuclear challenges that directly impact the climate.</li><li>
Build your career through technical fellowships, leadership roles, and global opportunities.</li><li>
Do your best work in a culture that values safety, innovation, and well-being.</li></ul><div></div><div> 
Framatome is an Equal Opportunity / Affirmative Action Employer. All qualified applicants receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.</div><div></div><div> 
Offers are contingent on background check and drug screen (where applicable). Positions may involve access to information subject to U.S. export control regulations; applicants must meet eligibility requirements (see DOE 810 Appendix A).</div></div>

Software Engineer III

Adobe

We are looking for a Staff Machine Learning Engineer to join our team of driven machine learning and software engineers. This role covers system design, prompt engineering, ML model evaluation, building data pipelines, prototype creation, and operationalization.
 The ideal candidate will have a strong background in both classical and deep learning, along with experience developing and deploying production-ready ML and GenAI solutions. If you enjoy shipping impactful, customer-facing features, experimenting with new technologies, and contributing to high-visibility projects with room for creativity and ownership, this role is for you. Our work is dynamic, collaborative, and data-driven.
 
What You’ll Do
 
Bring a 0→1 product mindset , helping shape ideas into real, measurable impact.
 
Design, build, and optimize backend services that power ML and Generative AI features.
 
Develop, evaluate, and deploy ML models using classical, deep learning, and GenAI approaches.
 
Contribute to agentic systems and orchestration frameworks that enable intelligent, multi-step reasoning and task automation.
 
Collaborate with cross-functional teams to integrate ML solutions into production workflows.
 
Analyze and improve the efficiency, accuracy, and scalability of AI-enabled systems.
 
Stay up to date with advancements in ML, GenAI, and prompt optimization research.
 
Mentor junior engineers and help grow the team’s technical depth.
 
What You Need to Succeed
 Required Qualifications
 
Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field.
 
10+ years of experience in machine learning engineering, applied research, or production ML systems.
 
Strong Python software engineering skills, including system design, clean architecture, testing, CI/CD, version control, and code review best practices.
 
Experience taking ML-powered features from 0→1 through production and ongoing iteration.
 
Hands-on experience deploying and monitoring ML models in production environments.
 
Experience designing or contributing to agentic architectures and multi-agent orchestration systems.
 
Strong understanding of classical ML, deep learning, and modern Generative AI techniques.
 
Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable ML deployment.
 
Solid foundation in data structures, algorithms, and distributed system design.
 
Comfortable leveraging AI coding agents to accelerate development workflows.
 
Excellent communication skills and demonstrated technical leadership experience.
 
Preferred Qualifications
 
Experience with Generative AI systems, prompt optimization frameworks, and LLM-as-a-judge / evaluation methodologies.
 
Deep understanding of Retrieval-Augmented Generation (RAG) and modern NLP pipelines.
 
Experience with MLOps tooling, experiment tracking, model lifecycle management, and observability frameworks.
 
Track record of mentoring engineers and influencing technical direction on high-visibility projects.
 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.
 
Let’s Adobe together
 At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture , focus on people, purpose and community , Adobe for All , comprehensive benefits programs , the stories we tell , the customers we serve, and how you can help us advance our mission of empowering everyone to create.
 
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 or call +1 408-536-3015.
 
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 $172,500 -- $306,625 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 $211,800 - $306,625
 
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
 There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability.
 
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.

Staff Machine Learning Engineer 5

Microsoft

<div>Overview<div> 
Microsoft Purview is redefining how data protection works in an AI-native world. As Copilot adoption accelerates and collaboration patterns evolve, protection can no longer live as complex policy configuration buried in portals. It must be intelligent, adaptive, and operational by default. We are looking for a Senior Machine Learning Engineer to build AI-native experiences at the core of our data security platform. This role focuses on applying Generative AI and applied machine learning to solve real customer problems in security, risk reasoning, and automation. You will design, build, and productionize AI systems that power risk scoring, reasoning layers, explainability, AI graders, and intelligent recommendations across multi-tenant SaaS environments. This is a hands-on role requiring strong depth in LLMs, applied ML systems, evaluation frameworks, and large-scale distributed architectures.</div><div>
You will work closely with Product, Applied Scientists, and Engineering teams to translate ambiguous product problems into robust, reliable, and trustworthy AI capabilities that operate at enterprise scale.</div><div>
This is not a research-only role &mdash; it is a build-and-ship role focused on measurable customer impact.</div> 
Microsoft&rsquo;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 
Responsibilities 
<ul><li>
Architect and productionize AI systems that power risk scoring, classifier-level reasoning, explainability narratives, and intelligent recommendations across multi-tenant SaaS environments.</li><li>
Design and build GenAI pipelines (RAG + agentic orchestration) including indexing, embedding strategies, retrieval optimization, prompt frameworks, tool integration, and memory/control flows tailored for security use cases.</li><li>
Own model evaluation and guardrails, defining offline/online metrics (precision, recall, risk calibration, hallucination control), building evaluation harnesses, and implementing responsible AI safeguards.</li><li>
Optimize model trade-offs (latency, cost, accuracy, safety) and deploy scalable inference systems with monitoring, drift detection, and feedback loops from customer signals.</li><li>
Partner with Product and Engineering to translate ambiguous security requirements into reliable AI-native workflows that are simple, trustworthy, and adoption-ready for SMB/SMC and Enterprise customers.</li></ul> 
Qualifications 
<ul><li>
8+ years of experience building and deploying machine learning systems in production environments.</li><li>
Strong hands-on experience with LLMs, RAG architectures, and applied Generative AI systems, including prompt design and grounding techniques.</li>
Proven expertise in designing end-to-end ML pipelines (data ingestion feature engineering training evaluation<li> serving).</li><li>
Experience defining and operationalizing offline and online evaluation frameworks (precision/recall, calibration, drift detection, A/B testing) and implementing guardrails for reliability and safety.</li><li>
Strong programming skills in Python, with experience in modern ML frameworks (e.g., PyTorch/TensorFlow) and cloud-based ML infrastructure; experience building multi-tenant SaaS AI systems with attention to scalability, latency, and cost optimization.</li><li>
Experience fine-tuning or adapting foundation models (e.g., LoRA/PEFT) and building agentic orchestration systems is preferred; knowledge of data security, compliance, or risk-scoring domains will be an additional advantage.</li></ul> 
 
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. 
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.</div>

Senior Machine Learning Engineer

<div>Why This Role Is Critical:
<div>
As the need for nuclear energy grows, the future couldn&rsquo;t be brighter. Join our vital mission to create lasting solutions for our planet&rsquo;s greatest challenges. In this role, you are ready to shape the future of clean energy and make a long-lasting global impact. With us, anticipate engaging and challenging projects that deliver innovative solutions and value-added technologies helping power more than 38 million North American homes.</div>
What You&rsquo;ll Do Day-To-Day:
<ul><li>Works from detailed programming specifications, designs, modifies, develops, writes and implements software-programming applications.</li><li>
Participates in the testing process through test review and analysis, test witnessing and certification of software.</li><li>
Supports and/or installs software applications/operating systems.</li><li>
Manages installation and configuration of third-party applications.</li><li>
Provides resolution to a fundamental problem or seizes an opportunity that improves the satisfaction of a customer, user or provider.</li><li>
Explains the financial impact of a solution or a decision to assist customers in complying with budget requirements.</li><li>
May assume ownership of a specific application. Ownership = operational leadership, strategic leadership, responsible for creating and defending software design, technical leadership within a task or project that changes the software, interface with IT, vendor/consultant/subcontractor/customer management.</li><li>
Work as an ML systems engineer building software for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current).</li><li>
Support the development of the ML workflows by ensuring that they can be deployed in reproducible, stable workflows for pilot use.
</li><li>Collaborate with subject-matter experts to validate solutions and follow best practices for packaging, observability, and integration into existing systems.</li></ul>
What You'll Bring:
<ul><li>Bachelor&rsquo;s Degree in Computer Science, Engineering.
</li><li>At least 2 years of related experience is required.</li><li>
At least 1 year in a hands-on role in a machine learning Engineering or Data Science context.</li><li>
Equivalent work experience may be considered in lieu of degree.</li><li>
Familiarity with Python</li><li>
Experience with implementation of structured logging, error reporting, and performance tracing to support debugging and diagnostics without overengineering full production infrastructure.</li><li>
Ownership of packaging of ML code for reproducible execution and deployment in air-gapped, workstation-class environments. This includes managing dependencies, configuration separation, environment isolation, and addressing runtime constraints and offline realities.</li></ul>
Total Rewards Package:
<ul><li>Salary: $77,000 - $100,000, and may also include annual incentives and performance bonuses. The base salary range is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location.</li><li>
Health &amp; Wellness: Multiple medical plan options, dental &amp; vision coverage, life insurance, long- and short-term disability, and optional supplemental plans (critical illness, accident, hospital indemnity, dependent life).</li><li>
Retirement: 401(k) with employer match.</li><li>
Paid Time Off: Up to 3 weeks of vacation, 8 sick days, and 13 paid holidays annually.</li><li>
Extra Perks: Lifestyle spending account, employer paid mental health support, education reimbursement, professional development opportunities, adoption assistance, parental leave, and employee assistance programs.</li></ul>
About Framatome:
<div>
Framatome has been shaping the future of nuclear energy in the U.S. for more than 60 years. Framatome Inc. is headquartered in Lynchburg, Virginia, with more than 2,400+ employees across North America supporting nearly every nuclear plant in the nation.</div><div></div><div> 
From our facilities in Virginia, Washington, North Carolina, Pennsylvania, Massachusetts and beyond, we deliver outage services, advanced nuclear fuel technologies, and next-gen reactor innovations that keep the lights on.</div><div></div><div> 
At Framatome, you will join a global team committed to creating reliable, carbon-free energy. Here, you can:</div><ul><li>
Solve complex nuclear challenges that directly impact the climate.</li><li>
Build your career through technical fellowships, leadership roles, and global opportunities.</li><li>
Do your best work in a culture that values safety, innovation, and well-being.</li></ul><div></div><div> 
Framatome is an Equal Opportunity / Affirmative Action Employer. All qualified applicants receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.</div><div></div><div> 
Offers are contingent on background check and drug screen (where applicable). Positions may involve access to information subject to U.S. export control regulations; applicants must meet eligibility requirements (see DOE 810 Appendix A).</div></div>

Software Engineer II

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