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ai ml modeling engineer i intern united states

Cisco

Please note this posting is to advertise potential job opportunities. This exact role may not be open today but could open in the near future. When you apply, a Cisco representative may contact you directly if a relevant position opens.
 
Applications are accepted until further notice.
 
Meet the Team
 
Join a dynamic team of engineers and data scientists focused on building the next generation of intelligent systems. We work at the intersection of software engineering and machine learning, developing scalable pipelines and models that power Cisco’s vast ecosystem. Our team thrives on collaboration, innovation, and the drive to turn complex data into seamless user experiences.
 
Your Impact
 
As an AI/ML Modeling Engineer Intern, you will apply your foundational knowledge of computer science and mathematics to solve real-world challenges. You will work closely with senior engineers to support the machine learning lifecycle, from data acquisition and cleaning to model testing and deployment. This role offers the opportunity to gain hands-on experience with production-grade AI tools while contributing to projects that impact millions of users worldwide.
 
<ul>
<li>Support Model Development: Assist in the design, implementation, and testing of machine learning models to solve specific business or technical problems.</li>
<li>Data Pipeline Engineering: Help build and maintain data preprocessing pipelines to ensure high-quality input for training and evaluation.</li>
<li>Performance Evaluation: Conduct experiments to measure model accuracy and efficiency, using standard metrics to document and report findings.</li>
<li>Collaborative Engineering: Work within an agile environment, participating in code reviews and contributing to the team’s shared codebase and technical documentation.</li>
</ul>
Minimum Qualifications
 
<ul>
<li>Currently enrolled in an undergraduate degree program. Relevant fields include: Computer Science, Data Science, Statistics, Electrical Engineering, or a related technical field.</li>
<li>Strong foundational knowledge of programming languages such as Python, Java, or C++, with experience using standard data analysis libraries like NumPy, pandas, or Matplotlib through coursework or personal projects.</li>
<li>Basic understanding of core machine learning concepts, including supervised and unsupervised learning, regression, and classification.</li>
<li>Solid mathematical background in linear algebra, calculus, and probability/statistics.</li>
<li>Able to legally live and work in the country for which you're applying, without visa support or sponsorship.</li>
</ul>
Preferred Qualifications
 
<ul>
<li>Experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.</li>
<li>Familiarity with version control systems like Git and collaborative development environments.</li>
<li>Basic knowledge of SQL and experience working with relational databases or structured data.</li>
<li>Previous experience with Jupyter Notebooks for data exploration and visualization.</li>
<li>Strong problem-solving skills and a passion for staying current with emerging AI and software engineering trends.</li>
</ul>
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:
 
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:
 
<ul>
<li>10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees</li>
<li>1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco</li>
<li>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</li>
<li>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)</li>
<li>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</li>
<li>Additional paid time away may be requested to deal with critical or emergency issues for family members</li>
<li>Optional 10 paid days per full calendar year to volunteer</li>
</ul>
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:
 
<ul>
<li>.75% of incentive target for each 1% of revenue attainment up to 50% of quota;</li>
<li>1.5% of incentive target for each 1% of attainment between 50% and 75%;</li>
<li>1% of incentive target for each 1% of attainment between 75% and 100%; and</li>
<li>Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.</li>
</ul>
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: 
$44,000.00 - $185,000.00Non-Metro New York state& Washington state:
 $44,000.00 - $185,000.00* 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.

AI/ML Modeling Engineer I (Intern)- United States

Glocomms

<div>
 
 
 Software Engineer, Data &amp; Machine Learning
 
 
 
 
 Location:
 
 Remote, United States
 
 
 
 Employment Type:
 
 Full-time
 
 
 
 Compensation:
 
 Starting at $110K
 
 
 
 
 
 We're looking for a Software Engineer, Data &amp; Machine Learning to join a growing engineering team focused on building intelligent, production-grade systems. This role is ideal for an engineer with hands-on experience applying machine learning and deep learning techniques to real-world problems, particularly in areas like computer vision and modern AI models.
 
 
 You'll work closely with other engineers and machine learning practitioners to design, train, and deploy models that operate at scale. This is a hands-on role with meaningful ownership over model development, experimentation, and integration into production systems.
 
 
 
 
 
 Key Responsibilities
 
 <ul>
 <li>
 Design, build, and optimize machine learning models for tasks such as image classification, object detection, or related vision problems
 </li>
 <li>
 Experiment with modern deep learning architectures, including convolutional models and transformer-based approaches
 </li>
 <li>
 Contribute to end-to-end ML workflows, from data preparation and training through evaluation and deployment
 </li>
 <li>
 Analyze large, complex datasets to improve model performance and reliability
 </li>
 <li>
 Write clean, well-documented, production-quality code and contribute to shared ML tooling and infrastructure
 </li>
 <li>
 Partner with cross-functional engineering teams to integrate models into live systems
 </li>
 <li>
 Stay current with emerging techniques in machine learning and applied AI
 </li>
 </ul>
 
 
 
 
 Qualifications
 
 <ul>
 <li>
 2-5 years of experience in Software Engineering, Machine Learning, or a related field
 </li>
 <li>
 Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or similar
 </li>
 <li>
 Hands-on experience with deep learning models, particularly in computer vision
 </li>
 <li>
 Solid understanding of supervised learning, model evaluation, and core ML concepts
 </li>
 <li>
 Experience with version control and modern development workflows
 </li>
 <li>
 Ability to work effectively in a collaborative, remote-friendly environment
 </li>
 <li>
 Strong problem-solving skills and a curiosity-driven mindset
 </li>
 <li>
 
 Permanent work authorization in the United States is required
 
 </li>
 </ul>
 
 
 
 
 Nice to Have
 
 <ul>
 <li>
 Exposure to transformer-based models or multimodal systems (e.g., vision + language)
 </li>
 <li>
 Familiarity with cloud platforms and containerized environments
 </li>
 <li>
 Experience supporting or deploying models in production
 </li>
 <li>
 Background working with experimentation, model tracking, or reproducible ML pipelines
 </li>
 <li>
 Interest in applying ML techniques to high-impact, real-world use cases
 </li>
 </ul>
 
 Compensation &amp; Benefits
 
 <ul>
 <li>
 Base salary starting at $110K
 </li>
 <li>
 Equity participation
 </li>
 <li>
 Comprehensive benefits package
 </li>
 <li>
 Fully remote role within the United States
 </li>
 <li>
 Emphasis on ownership, technical growth, and modern engineering practices
 </li>
 </ul>
 
 
 
 
 Location Notes
 
 
 This role is fully remote, but candidates must be based in San Francisco, New York City area, Seattle, Miami, or Austin, TX. The company is building teams in selective talent hubs to maintain convenient access to in-person collaboration when needed and to reduce exposure to future return-to-office mandates.
 
 
 
 
 
 At this time, the employer is unable to provide visa sponsorship or support visa transfers. Candidates must already have permanent work authorization in place.
 
</div>

Data Scientist, Computer Vision

Intercom

<div><div>Intercom is the AI Customer Service company on a mission to help businesses provide incredible customer experiences.

Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Fin can also be combined with our Helpdesk to become a complete solution called the Intercom Customer Service Suite, which provides AI enhanced support for the more complex or high touch queries that require a human agent.

Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service. Driven by our core values, we push boundaries, build with speed and intensity, and consistently deliver incredible value to our customers.</div><div><h2 class="jobSectionHeader">
What's the opportunity?</h2>

Intercom is building a GTM Data Products team to embed machine learning and AI directly into our Sales and Marketing workflows.

We are hiring a Senior GTM Data Scientist to design and deploy predictive systems that materially improve:
<ul>
<li>Customer acquisition (e.g. via lead scoring, attribution)</li>
<li>Sales efficiency (e.g. via book carves, sales quotas)</li>
<li>Customer retention and expansion (e.g. via revenue prediction)</li>
</ul>
This is not a reporting role.

This role owns end-to-end data products - from problem framing and modeling to deployment and operational integration - that directly influence how our GTM organization prioritizes leads, manages accounts, allocates resources, and drives revenue.

You&rsquo;ll work closely with Marketing, Sales, and RevOps leadership to build ML-powered systems that change how decisions are made at scale.

If you are excited about building applied machine learning systems that generate measurable revenue impact, this role is for you.
<h2 class="jobSectionHeader">
What will I be doing?</h2>
<div><ul><li>Build Revenue-Impacting ML Systems</li></ul>
</div><ul><li>Develop, deploy, optimize predictive models (lead scoring, account prioritization, marketing attribution, revenue estimation)</li>
<li>Productionize models into operational systems (Salesforce, Marketo, outbound workflows)</li>
<li>Monitor model performance and iterate for measurable business lift</li>
<li>Design and implement experimentation frameworks (A/B testing, holdouts, incremental lift measurement)</li>
<li>Apply advanced techniques when appropriate (e.g., causal inference, uplift modeling, segmentation, LTV modeling)</li>
</ul>
You don&rsquo;t just build models - you ensure they change behavior.
<div>
<ul><li>Own End-to-End Data Products</li></ul>
</div><ul><li>Translate ambiguous business problems into clear, measurable objectives</li>
<li>Define GTM data products vision, success metrics, and roadmap</li>
<li>Ensure integration into existing workflows and systems</li>
<li>Lead stakeholder alignment and change management</li>
<li>Secure buy-in from system owners before replacing or enhancing existing solutions</li>
</ul>
You operate as a mini GM for your data products.

 3. Architect Scalable Data Foundations
<ul>
<li>Design robust data pipelines and modeling infrastructure in collaboration with Data Engineering / Data Infrastructure</li>
<li>Ensure data quality, governance, and reproducibility</li>
<li>Elevate the team&rsquo;s standards for experimentation, documentation, and knowledge sharing</li>
<li>Push adoption of new tools and AI capabilities where appropriate</li>
</ul>
You raise the technical bar for the GTM organization.
<h2 class="jobSectionHeader">
What impact might I have?</h2>

Within 6-12 months, you might:
<ul>
<li>Launch predictive models that materially improve conversion, expansion, or retention</li>
<li>Reduce inefficiencies in Sales workflows through automation</li>
<li>Help leadership make investment decisions backed by rigorous data science</li>
<li>Influence GTM strategy through quantitative insight and modeling</li>
</ul>
Success is measured in business outcomes - not dashboards built.
<h2 class="jobSectionHeader">
What we&rsquo;re looking for</h2>

Experience
<ul>
<li>5+ years in Data Science, Applied ML, or Advanced Analytics</li>
<li>Experience building predictive models deployed into production environments</li>
<li>Experience working with Sales, Marketing, or GTM teams in a B2B SaaS environment preferred</li>
<li>Proven track record influencing senior stakeholders through data</li>
</ul>
Technical Skills
<ul>
<li>Expert-level SQL</li>
<li>Advanced Python or R for modeling and experimentation</li>
<li>Strong foundation in statistics and experimental design</li>
<li>Experience with:</li>
<li><ul><li>Predictive modeling</li>
<li>Feature engineering</li>
<li>Model evaluation &amp; validation</li>
<li>Causal inference or uplift modeling (strong plus)</li>
<li>Model deployment &amp; monitoring (strong plus)</li>
</ul></li>
</ul>Mindset &amp; Leadership

You are:
<ul>
<li>A trusted advisor who influences strategy, not just execution</li>
<li>Deeply curious about the &ldquo;why&rdquo; behind business metrics</li>
<li>Comfortable operating with autonomy in ambiguous environments</li>
<li>AI-first: you look for opportunities to automate, optimize, and scale</li>
<li>Clear and compelling in communication - you turn complex models into strategic decisions</li>
<li>Impact-oriented: you prioritize work that moves revenue</li>
</ul><h2 class="jobSectionHeader">Benefits</h2>

We are a well-treated bunch with awesome benefits! If there&rsquo;s something important to you that&rsquo;s not on this list, talk to us! :)
<ul>
<li>Competitive salary and meaningful equity</li>
<li>Comprehensive medical, dental, and vision coverage</li>
<li>Regular compensation reviews - great work is rewarded!</li>
<li>Flexible paid time off policy</li>
<li>Paid Parental Leave Program</li>
<li>401k plan &amp; match</li>
<li>In-office bicycle storage</li>
<li>Fun events for Intercomrades, friends, and family!</li>
</ul>
<ul><li>Proof of eligibility to work in the United States is required</li></ul>

The base salary range for candidates within the San Francisco Bay Area is $197,600 - $246,713. Actual base pay will depend on a variety of factors such as education, skills, experience, location, etc. The base pay range is subject to change and may be modified in the future. All regular employees may also be eligible for the corporate bonus program or a sales incentive (target included in OTE) as well as stock in the form of Restricted Stock Units (RSUs).
#LI-Hybrid</div><div>
Policies

Intercom has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week.

We have a radically open and accepting culture at Intercom. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values.
Intercom values diversity and is committed to a policy of Equal Employment Opportunity. Intercom will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, age, physical or mental disability, veteran or military status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other legally recognized protected basis under federal, state, or local law.</div></div>

Senior GTM Data Scientist New

Aarki

<div>
 
 Who are we?
 
 
 
 Aarki is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
 
 
 
 The role?
 
 
 
 We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
 
 
 
 What will you do?
 
 
 
 <ul>
 <li>
 Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
 </li>
 <li>
 Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
 </li>
 <li>
 Analyze the impact of integrating new data sources and features into our models.
 </li>
 <li>
 Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
 </li>
 <li>
 Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
 </li>
 <li>
 Document experiments, assumptions, and outcomes; maintain reproducibility
 
 
 </li>
 </ul>
 
 What are we looking for?
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Mathematics, Physics, Computer Science, or a related technical field.
 </li>
 <li>
 At least 2 years of professional experience in machine learning, statistical analysis, and data analysis.
 </li>
 <li>
 Experience with machine learning techniques such as regression, classification, and clustering.
 </li>
 <li>
 Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
 </li>
 <li>
 Strong grasp of probability, statistics, and data analysis principles.
 </li>
 <li>
 Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
 
 
 </li>
 </ul>
 Nice-to-Have
 
 
 <ul>
 <li>
 Familiarity with system programming languages including C++ and Rust is a plus.
 </li>
 <li>
 Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
 </li>
 <li>
 Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.
 </li>
 </ul>
</div>

Machine Learning Engineer

Handshake

<div>
 
 About Handshake
 
 
 
 Handshake is the career network for the AI economy. More than 20 million knowledge workers, 1,600 educational institutions, and 1 million employers &mdash; including 100% of the Fortune 50 &mdash; trust Handshake to power career discovery, hiring, and upskilling. From freelance AI training gigs to first internships to full-time careers and beyond, we connect talent with opportunity at every stage.
 
 
 This unique position in the ecosystem is driving exceptional growth &mdash; in 2025, we tripled ARR at scale.
 
 
 
 Why Join Handshake Now
 
 
 
 <ul>
 <li>
 Shape how careers evolve in the AI economy at global scale, with visible real-world impact
 </li>
 <li>
 Work directly with leading AI labs, Fortune 500 partners, and top educational institutions
 </li>
 <li>
 Help build a rapidly scaling business on a path toward multi-billion-dollar revenue
 
 
 </li>
 </ul>
 
 The Role
 
 
 
 As a
 
 AI/Machine Learning Engineering Intern
 
 , you will contribute to building intelligent product experiences that help students discover and secure opportunities. Your work will span search, recommendations, matching, and other discovery systems that power job exploration on Handshake.
 
 
 You will gain hands-on experience developing, evaluating, and deploying machine learning models in a production environment, learning how large-scale ML systems are designed, optimized, and maintained.
 
 
 This is a paid, full-time summer internship with two cohort options:
 
 
 <ul>
 <li>
 May 18 &ndash; August 7, 2026
 </li>
 <li>
 June 15 &ndash; September 4, 2026
 
 
 </li>
 </ul>
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Partner with senior engineers and data scientists to develop machine learning models that improve user experience
 </li>
 <li>
 Build Agentic pipelines/workflows to improve the Handshake student/employer user experience
 </li>
 <li>
 Contribute to experimentation, model evaluation, and performance monitoring
 </li>
 <li>
 Participate in technical discussions, brainstorming sessions, and team reviews
 </li>
 <li>
 Document methodologies and findings to support knowledge sharing and long-term system improvements
 
 
 </li>
 </ul>
 
 You Have
 
 
 
 
 Must Haves
 
 
 
 <ul>
 <li>
 Are currently pursuing a degree in Computer Science, Data Science, or a related field
 </li>
 <li>
 Have strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow
 </li>
 <li>
 Have exposure to software engineering best practices (version control, testing, code reviews)
 </li>
 <li>
 Have familiarity with data analysis techniques and experience with SQL
 </li>
 <li>
 Have strong problem-solving skills and the ability to work in a collaborative team environment
 </li>
 <li>
 Have strong communication skills and are able to explain technical concepts effectively
 
 
 </li>
 </ul>
 
 Bonus Points
 
 
 
 <ul>
 <li>
 Experience with cloud platforms such as AWS, Google Cloud, or Azure
 </li>
 <li>
 Experience with modern coding tools like Cursor/Claude code/Codex
 </li>
 <li>
 Prior internship or project experience in applied machine learning in domains such as NLP, search and recommendation systems
 
 
 </li>
 </ul>
 
 We Offer
 
 
 
 Handshake provides benefits that help you feel supported and thrive at work and in life.
 
 
 (The below benefits apply to US-based interns.)
 
 
 <ul>
 <li>
 💰 Competitive hourly compensation
 </li>
 <li>
 📚 Mentorship and hands-on learning from experienced ML engineers
 </li>
 <li>
 💻 5 days/week in-office experience
 </li>
 <li>
 🤝 Structured intern programming and team events
 
 
 </li>
 </ul>
 Explore our mission, values, and open roles at
 
 joinhandshake.com/careers
 
 .
 
 
</div>

AI/Machine Learning Engineer Intern

Salesforce

<div>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
We are seeking an experienced Salesforce Software Quality Lead Member of Technical Staff (LMTS) with a strong technical background. This engineer should have an AI first approach and be able to help the team translate from traditional testing to using AI in driving acceleration and accuracy in testing and be a multiplier using AI tools. Prior experience as a member of a Data Science, ML Science, or ML Engineering team is a plus.
As a Quality LMTS, you will play a key role in defining, implementing, and maintaining the quality strategy for Salesforce products, with a strong focus on integrating cutting-edge AI technologies. You will collaborate with cross-functional teams, including product development, data science, and AI teams, to ensure that Salesforce&rsquo;s AI-driven solutions meet the highest quality standards for enterprise applications.
<h2 class="jobSectionHeader">This role requires onsite presence in San Francisco, California office.
</h2>Key Responsibilities: 
 
Drive Innovation &amp; Adoption: Identify opportunities for integrating AI/ML solutions into existing workflows, propose new ones that are high-quality and efficient, and generally positively influence engineering decision-making by communicating and evaluating solution options, and facilitating agreement among key stakeholders. Ensure that we are continuously raising our standard of engineering excellence and producing high business impact. 
 
Continued Excellence in AI/ML: Be tuned into developments in the field of AI/ML, by tracking publicly available models, frameworks, libraries and publications, while being focused on their practical application and relevance to Salesforce. 
 
Technical Leadership on Proof of Concepts: Influence the direction of R&amp;D as a whole through your technical, process, or product knowledge leadership. Industry-expert level in understanding of quality concerns and validation techniques. Able to drive behaviors and initiatives that both focus on quality and throughput. 
 
Collaborate on AI Model Improvements: Work closely with data scientists and machine learning engineers to provide feedback on LLM performance and recommend improvements based on quality testing results. 
 
Evaluate LLM Performance: Assess and optimize Large Language Models (LLMs) for quality, accuracy, relevance, and safety in various use cases.
 
Human Evaluation: Oversee human evaluation processes for subjective quality dimensions, such as response engagement, user safety, and contextual accuracy. 
 
Define Quality Strategies: Develop and implement robust quality strategies for Salesforce&rsquo;s SaaS products, focusing on AI/ML and LLM integrations. 
 
Multiplier: Provide technical leadership for critical areas that significantly impact customer success. You have depth of expertise in key technologies and you are often consulted on the design and delivery of new solutions. You bring new best practices to R&amp;D and actively ensure that they are being used.
Continues to deepen and widen their understanding of the application and drives discussion of sometimes complex or controversial issues in open forums such as concept reviews, VAT discussions, and cross team discussions. 
 
Cross-Platform Collaboration: Use your understanding of customers&rsquo; needs across industries and multiple technology landscapes (CRM, Modern Data Stack, Analytics &amp; BI, CRM and AI) to develop solutions across Salesforce's technology stack. Work in a consultative fashion to improve communication, teamwork and alignment among teams inside and outside of the organization. 
 
Mentor and Organization Builder: Be a cornerstone in the infrastructure of technical expertise represented by the organization's senior engineers. You challenge and engage with them to develop their expertise and leadership contributions. You are a key resource for engineers seeking to advance to the next level.
Required Skills and Qualifications:
<ul><li>Having an overall 10+ years of experience with software development engineer in test or quality engineering experience.
</li><li>Proficiency in Java programming languages, with hands-on experience in testing frameworks.
</li><li>AI &amp; Machine Learning Expertise: Strong understanding of AI/ML principles, particularly in evaluating and optimizing model performance, response consistency and safety, fine-tuning of Small Language Models and model distillation. Although the focus is LLMs, the candidate is also expected to be familiar with non-LLM AI/ML techniques and practices such as model selection, picking of appropriate metrics, embeddings, areas such as fairness and explainable AI, traditional models such as Random Forest, Gradient Boosting, etc. Experience with libraries such as scikit, Transformers (Hugging Face), Pytorch and TensorFlow.
</li><li>LLM Expertise: Hands-on experience evaluating Large Language Models (LLMs) for quality response, including familiarity with GPT-like models and AI evaluation frameworks.
</li><li>Focus on Delivery: Ability to deliver practical solutions for an active user base, which you are expected to follow-up with enhancements and improvements, as needed.
</li><li>Collaboration &amp; Communication: Excellent communication skills, with the ability to work cross-functionally with product managers, AI/ML engineers, and data scientists.
</li><li>Problem-Solving &amp; Analytical Skills: Strong analytical and problem-solving abilities with a focus on identifying quality gaps and driving improvement.
</li><li>Familiarity with API testing tools (e.g., Postman, SoapUI).
</li><li>Basic knowledge of SQL for database validation.
</li><li>Fluent with automation technologies such as JUnit, Jest, Selenium and Jenkins
</li><li>Experience with developer tools like git, Maven and Eclipse IDE
</li><li>Solid understanding of REST principles
</li><li>Excellent written and verbal communication skills.
</li><li>A related technical degree required.
</li></ul>Preferred Qualifications:
<ul><li>Prior experience in a Data Science or a Machine Learning Science or a Machine Learning Engineering team.
</li><li>Experience in ethical AI practices and evaluating models for interpretability, fairness, bias, and user safety.
</li><li>Strong understanding of public cloud infrastructure - AWS/Azure/GCP; Certification in Salesforce.
</li>Can design, implement, test and deliver AI/ML-based frameworks for highly scalable products. 
<li>
</li></ul> 
Unleash Your Potential
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At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.&amp;#xa;&amp;#xa;The typical base salary range for this position is $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually.&amp;#xa;&amp;#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.</div>

Lead Software Engineer - Quality & AI

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

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