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ml ai engineering intern

Bain & Company

What Makes Us A Great Place To Work
 
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on [1] Glassdoor’s Best Places to Work list and have earned the #1 spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
 
About Bain AI, Insights & Solutions (AIS)
 
Bain’s [2] AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.
 
The Impact You’ll Have
 
Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.
 
As an ML-AI Engineer Intern in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.
 
The Role
 
We are hiring an ML-AI Engineer Intern to help build GenAI and agentic AI applications for enterprise use cases, ranging from rapid proofs of concept (POCs) to MVPs and, where appropriate, scaled production deployments. You will design and implement LLM-driven applications and agentic workflows that use tools, data, and enterprise systems to execute multi-step tasks reliably and safely.
 
While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed, including building evaluation approaches, working with data pipelines, and developing or integrating ML models when they materially improve performance or reliability.
 
You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.
 
Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.
 
What You’ll Do
 
Build AI applications that drive real business outcomes
 <ul>
<li>Contribute to the design and development of GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.</li>
<li>Support the implementation of agentic workflows where they add clear value (e.g., tool use, multi-step execution, human-in-the-loop controls), with attention to reliability, safety, and clear failure modes.</li>
<li>Assist in building robust agent capabilities including context engineering, memory/state management (short-term and long-term), orchestration, routing, and tool integration patterns.</li>
</ul>
Build and apply data science and machine learning capabilities
 <ul>
<li>Contribute to ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/testing, and performance analysis.</li>
<li>Apply appropriate ML methods for the problem, spanning classical ML and deep learning (including sequence, text, and image models when relevant).</li>
<li>Develop working knowledge of modern deep learning concepts, including transformer fundamentals and LLM pre-training vs post-training concepts (e.g., instruction tuning, preference optimization).</li>
</ul>
Engineer for real delivery: POC → MVP → production
 <ul>
<li>Write clean, testable, maintainable code and ship AI services through the full SDLC: build → test → deploy → monitor → iterate.</li>
<li>Support implementation of MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.</li>
<li>Contribute to evaluation and observability for GenAI and agentic systems: tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt/policy optimization.</li>
<li>Gain exposure to secure enterprise deployment practices, including access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.</li>
<li>Contribute to reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.</li>
</ul>
Thrive in a consulting environment
 <ul>
<li>Communicate clearly with technical and non-technical audiences; present in working sessions, share recommendations, and help write crisp technical documentation.</li>
<li>Support client discussions, demos, and documentation development as part of broader engagement teams.</li>
</ul>
What We’re Looking For (Qualifications)
 
Educational background
 <ul>
<li>Currently pursuing or recently completed a bachelor’s, master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, or a related quantitative field; or equivalent early-career industry experience.</li>
<li>Strong academic performance or demonstrated excellence through research, projects, internships, competitions, or open-source contributions.</li>
</ul>
Core engineering + AI application skills
 <ul>
<li>0-2+ years of professional AI / ML engineering experience or equivalent experience through internships, research, academic projects, or industry roles, with strong backend engineering fundamentals.</li>
<li>Proficiency in Python and some experience building APIs/services (e.g., REST/gRPC) or comparable project-based experience.</li>
<li>Hands-on experience (academic, internship, or industry) building ML or LLM-powered applications, preferably with exposure to delivery considerations such as latency, cost, reliability, or security.</li>
<li>Exposure to retrieval/search systems (e.g., hybrid retrieval, vector search, reranking), and familiarity working with structured and unstructured data stores.</li>
<li>Exposure to agentic patterns (context management, tool integration, orchestration, memory/state handling); hands-on experience (academic, internship, or industry) preferred.</li>
<li>Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.</li>
</ul>
Breadth of knowledge across data science and machine learning
 <ul>
<li>Experience training, validating, and testing ML models; strong understanding of overfitting, generalization, and evaluation methodology.</li>
<li>Practical experience with feature engineering and data preprocessing.</li>
<li>Familiarity with a broad set of ML algorithms (classical ML and deep learning), and the ability to choose methods that match constraints.</li>
</ul>
Delivery mindset and consulting skills
 <ul>
<li>Excellent interpersonal and communication skills, able to explain technical decisions, tradeoffs, and results to mixed audiences.</li>
<li>Interest in applying AI to solve real-world business problems in a client-facing environment.</li>
</ul>
Working Model & Travel
 <ul>
<li>This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.</li>
<li>Travel may be required beyond your home office / primary working location. Frequency and destination vary by project needs.</li>
</ul>
U.S. Compensation Information
 
For all locations, the good-faith reasonable annualized full-time compensation for this role is commensurate with competitive geographic market rates. Additionally, in some locations compensation may vary based on several factors including, but not limited to relevant experience, education, licensure/certifications, training and skill level.
 
Compensation for this role in the United States includes a monthly base salary of $9,000 and Bain's best-in-class benefits package (details listed below).
 
<ul>
<li>Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.</li>
<li>Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck</li>
</ul>
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.
 References
 
Visible links
 1. https://www.bain.com/about/awards-recognition/
 2. https://www.bain.com/careers/work-with-us/integrated-teams/ai-insights-solutions-ais/

ML-AI Engineering Intern

Hayden AI

<div>
 
 About Us
 
 
 
 At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
 
 
 From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
 
 
 
 Job Title:
 
 Machine Learning Operations Engineer
 
 
 
 Company:
 
 Hayden AI Technologies, Inc.
 
 
 
 Location:
 
 460 Bryant Street, Suite 100, San Francisco, CA 94107
 
 
 
 Position Duties
 
 
 
 <ul>
 <li>
 Optimize orchestration processes to ensure efficient deployment and management of AI models.
 </li>
 <li>
 Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance.
 </li>
 <li>
 Upgrade throughput to enhance scalability and responsiveness of AI systems.
 </li>
 <li>
 Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency.
 </li>
 <li>
 Ship new features and updates rapidly while maintaining high levels of quality and reliability.
 </li>
 <li>
 Deploy and monitor machine learning models produced by deep learning engineers.
 </li>
 <li>
 Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility.
 </li>
 <li>
 Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions.
 </li>
 <li>
 Maintain accurate and updated software development documentation.
 
 
 </li>
 </ul>
 
 Degree Requirements
 
 
 
 Bachelor of Science or Engineering degree or a foreign equivalent in Robotics, Machine Learning, Computer Science, Electrical Engineering, Electronics and Telecommunication Engineering, or a related field.
 
 
 
 Experience Requirement
 
 
 
 One (1) year of work experience in the job offered or as a Deep Learning Engineer, Perception Engineer II, MLOps, Research Engineer II, Machine Learning, Computer Vision Intern, or another related deep learning engineer role.
 
 
 
 Other Special Requirements
 
 
 
 <ul>
 <li>
 One (1) year of work experience with all of the following:
 </li>
 <ul>
 <li>
 Deploying real-world applied computer vision (including deep learning models) on edge devices
 </li>
 <li>
 Python programming and software design
 </li>
 <li>
 C++
 </li>
 <li>
 Software development tools and libraries including PyTorch, OpenCV, TensorFlow, MLflow
 </li>
 <li>
 Automated data annotation
 </li>
 <li>
 Distributed training in the cloud
 </li>
 <li>
 Deploying and managing GPU clusters for ML pipelines and workflows
 
 </li>
 </ul>
 </ul>
 
 Rate of Pay:
 
 $135,699.00 to $190,000.00 per year
 
 
 
 Location of Position and Interview:
 
 
 
 Hayden AI Technologies, Inc.
 
 
 460 Bryant Street, Suite 100
 
 
 San Francisco, CA 94107
 
 
 
 Applicants Should Submit Resumes To
 
 
 
 Janet Le-Mcintosh
 
 
 janet.le@hayden.ai
 
 
 Hayden AI Technologies, Inc.
 
 
 460 Bryant Street, Suite 100
 
 
 San Francisco, CA 94107
</div>

Machine Learning Operations Engineer

Realign

<div>San Francisco, California 94016 Posted March 12th, 2026
 <div></div>Looking for more job opportunities? Click here!</div>
 <div>Job Type: Contract
Job Category: IT
Job Description
Role: Sr. AI/ML Engineer<ul><li> Experience &ndash; 10+ Years</li><li> Location - San Francisco, CA &ndash; LOCALS Only</li></ul><div>
Required Qualifications 
<ul><li>Deep expertise in Machine Learning, Generative AI, LLMs, RAG, and AI orchestration.</li><li>Strong understanding of Azure OpenAI, AWS Bedrock, or similar platforms.</li><li>Experience deploying AI in regulated environments.</li><li>Hands-on architectural and engineering leadership capabilities.</li><li>Strong communication skills with VP/Senior Director stakeholders.</li></ul>
 Preferred Qualifications 
<ul><li>12+ years in AI/ML, data engineering, or digital transformation.</li><li>Prior experience leading enterprise AI programs.</li><li>Utility domain experience (wildfire, grid, operations).</li><li>Knowledge of Responsible AI frameworks (NIST AI RMF, ISO 42001).</li></ul> 

Roles &amp; Responsibilities 
Role Overview 
Utilities are accelerating their enterprise AI adoption across Operations, Customer Experience, Wildfire Safety, Regulatory, and IT. The AI Lead will serve as the strategic driver for enterprise AI transformation&mdash;defining vision, governance, enterprise architecture, and leading delivery of high-impact GenAI and ML solutions.</div><div> 
Key Responsibilities 
<ul><li>Define the enterprise AI vision, roadmap, and operating model.</li><li>Identify and prioritize AI/ML/GenAI opportunities across business units.</li><li>Drive design and deployment of AI solutions including consultation agents, hazard detection, regulatory Q&amp;A automation, and contact center assistants.</li><li>Establish AI governance, compliance, model risk management, and auditability frameworks.</li><li>Lead cross-functional AI programs spanning IT, Operations, Customer Care, Regulatory, Safety, and Field Ops.</li><li>Mentor and guide data scientists, ML engineers, architects, and product owners.</li><li>Promote AI literacy, change management, and best practices across the enterprise.</li></ul></div>
Required Skills
DEVOPS ENGINEER SENIOR EMAIL SECURITY ENGINEER</div>

Sr. AI/ML Engineer-6

Apple

Are you excited about using AI/ML models to drive innovation across disciplines? Are you passionate about developing intelligent agents that transform how engineering decisions are made? If so, please come and join us! 
 
We are a multi-functional team focused on building platforms and tools that enhance the engineering design processes behind millions of Apple products-including iPhone, iPad, Apple Watch, AirPods, Vision Pro, and more. This is a unique opportunity to apply your AI/ML expertise and software development skills to have a real impact at scale. 
 
Description 
 
We are seeking a top-tier AI Application Engineer with strong architectural thinking, deep AI/ML knowledge, and robust software development skills to help design, build, and deploy intelligent systems powered by machine learning and large language models (LLMs). 
 
In this role, you'll work at the intersection of applied AI and software engineering-translating ground breaking research into practical, scalable tools. You&rsquo;ll take ideas from feasibility studies and rapid prototypes to production-ready solutions with intuitive, high-quality user experiences. Your contributions will shape how engineering teams make faster, smarter decisions across many Apple hardware programs.&quot;,&quot;responsibilities&quot;:&quot;Serve as a founding engineer to shape the project&rsquo;s technical foundation. 
 
Partner with hardware teams to define AI product features and scope. 
 
Benchmark, adapt, and integrate AI/ML models into existing software systems. 
 
Architect and implement the end-to-end software solution. 
 
Deploy and support AI tools for hardware engineering teams. 
 
Preferred Qualifications 
 
Hands-on experience developing AI-powered applications 
 
Experience designing and deploying agentic AI systems 
 
Extensive knowledge with popular LLMs such as Gemini, Claude, and GPT 
 
Experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) 
 
Demonstrated ability to bring innovative AI research into production systems 
 
Hands-on experience with cloud services and containerization technologies 
 
Additional experience in Java or C++ is a plus 
 
Master's or PhD in Computer Science, Computer Engineering, or a related field 
 
Minimum Qualifications 
 
BS and a minimum of 3 years relevant industry experience 
 
Strong understanding of generative AI models, particularly large language models (LLMs) 
 
Proficiency in Python 
 
Excellent software engineering skills, versed in modular and object-oriented design methodology and general SW standard processes 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $210,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AI Application Engineer

J&T Business Consulting

<div><div><div>Company: Confidential Client 
</div><div>Location: San Francisco, CA (Hybrid) 
</div><div>Salary: $240,000 &ndash; $380,000 base + equity + bonus 
</div><div>Recruitment Partner: J&amp;T Recruitment &ndash; Exclusively Retained 
</div>J&amp;T Recruitment has been exclusively retained by a rapidly scaling AI technology company in San Francisco that is building cutting-edge generative AI platforms used by global enterprise clients. 
They are seeking a Senior AI Engineer to design and deploy production-grade machine learning systems and contribute to the architecture of next-generation AI products.<h3 class="jobSectionHeader"> 
Key Responsibilities</h3> 
<ul><li>Design, train, and deploy large-scale machine learning and generative AI models</li> 
<li>Build scalable data pipelines for model training and inference</li> 
<li>Develop production-ready ML services and APIs</li> 
<li>Work closely with product managers and data scientists to translate research into production systems</li> 
<li>Optimize models for performance, cost, and latency</li> 
<li>Contribute to the design of distributed AI infrastructure</li> 
<li>Stay up to date with advancements in LLMs, transformer architectures, and generative AI</li> 
</ul><h3 class="jobSectionHeader">Tech Stack</h3> 
<ul><li>Python</li> 
<li>PyTorch / TensorFlow</li> 
<li>Hugging Face / Transformers</li> 
<li>Kubernetes</li> 
<li>AWS / GCP</li> 
<li>Vector databases</li> 
<li>ML pipelines (Airflow / Kubeflow)</li> 
</ul><h3 class="jobSectionHeader">Requirements</h3> 
<ul><li>5+ years of experience in Machine Learning or AI Engineering</li> 
<li>Strong experience building and deploying ML models to production</li> 
<li>Experience working with LLMs, NLP, or generative AI systems</li> 
<li>Strong programming skills in Python</li> 
<li>Experience with distributed systems and cloud platforms</li> 
</ul><h3 class="jobSectionHeader">Interview Process</h3> 
<div><ul><li>Recruiter Screening (30 minutes) &ndash; Introduction via J&amp;T Recruitment</li></ul> 
<ul><li>Technical Coding Interview (60 minutes) &ndash; Python and ML fundamentals</li></ul> 
<ul><li>Machine Learning System Design (90 minutes) &ndash; Designing scalable ML architecture</li></ul> 
<ul><li>Technical Deep Dive (90 minutes) &ndash; Discussion around previous AI projects</li></ul> 
<ul><li>Final Leadership Interview (45 minutes) &ndash; Cultural and team alignment</li></ul></div></div></div><div></div>

Senior AI Engineer

Sutter Health

We are so glad you are interested in joining Sutter Health!
 
Organization: 
SHSO-Sutter Health System Office-Bay
 
Position Overview: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
Responsible for leading the design, build, and maintain Sutter Health’s artificial intelligence (AI) and machine learning (ML) infrastructure, including end to end pipelines for ML and large language models (LLM) that support analytics, data science, and enterprise AI use cases. This includes how AI models and associated data are ingested, processed, trained, deployed, monitored, governed, and secured across cloud and on premises environments. Ensures that AI systems meet high standards of performance, reliability, and compliance, enabling the organization to safely and effectively integrate AI capabilities into clinical, operational, and strategic workflows.
 
Utilizing modern artificial intelligence operations (AIOps) and machine learning operations (MLOps) practices, leads the operationalization of models, maintenance of scalable AI services, monitoring of system behavior, and automation of deployment workflows. Works with structured and unstructured data, clinical data models, healthcare data standards, and modern cloud platforms. Develops and validates ML models, integrates researcher built or vendor provided algorithms, and contributes to AI platform architecture and tooling. Sets standards for high quality, secure, and well governed AI systems that support advanced analytics, automation, and intelligent applications.
 
Job Description: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
EDUCATION: 
<ul>
<li>Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field; or equivalent combination of education and experience.</li>
</ul>
TYPICAL EXPERIENCE: 
<ul>
<li>5 years recent relevant experience</li>
</ul>
PREFERRED EXPERIENCE: 
<ul>
<li>Advanced experience with Azure cloud services, including containerized model hosting, Azure ML, secure environment management, and cloud‑native deployment patterns.</li>
<li>Strong proficiency in MLOps / LLMOps practices, including CI/CD pipelines, automated testing, observability, model versioning, canary/AB rollouts, and drift monitoring.</li>
<li>Hands‑on expertise in building and operating AI/ML/LLM pipelines (ingestion → preprocessing → training → inference → monitoring), using Python, modern container frameworks, and orchestration systems.</li>
<li>Experience integrating researcher‑built or vendor‑provided ML/LLM models into production workflows, including API‑based embedding, performance tuning, and secure data handling.</li>
<li>Familiarity with healthcare data standards and environments, such as clinical data models, unstructured clinical text, and privacy/security expectations in regulated domains.</li>
</ul>
SKILLS AND KNOWLEDGE: 
<ul>
<li>Relevant experience in AI/ML Engineering and LLM/MLOps</li>
<li>Experience building end to end AI/ML pipelines, including training, deployment, monitoring, and retraining loops.</li>
<li>Strong programming skills in Python and familiarity with modern ML/LLM frameworks and libraries.</li>
<li>Hands on experience with Azure, including services such as Fabric, Foundry, Container Apps, AKS, and Application Insights.</li>
<li>Experience implementing AIOps / MLOps / LLM Ops practices, including CI/CD pipelines, automated testing, observability, and versioned deployments.</li>
<li>Experience with GitOps principles using GitHub Actions or similar tools.</li>
<li>Understanding of healthcare data models, including Epic Clarity, FHIR, HL7, and Caboodle.</li>
<li>Ability to evaluate and integrate machine learning models into production systems and support model lifecycle management.</li>
<li>Strong debugging skills across distributed systems, containerized environments, and cloud platforms.</li>
<li>Ability to work in cross functional environments with clinicians, data scientists, architects, and engineering teams.</li>
<li>Ability to produce high quality documentation and communicate complex technical concepts to diverse stakeholders.</li>
<li>Detail oriented, organized, and effective at prioritizing multiple concurrent initiatives.</li>
<li>Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.</li>
</ul>
These Principal Accountabilities, Requirements and Qualifications are not exhaustive but are merely the most descriptive of the current job. Management reserves the right to revise the job description or require that other tasks be performed when the circumstances of the job change (for example, emergencies, staff changes, workload, or technical development).
 
Job Shift: 
Days
 
Schedule: 
Full Time
 
Days of the Week: 
Monday - Friday
 
Weekend Requirements: 
As Needed
 
Benefits: 
Yes
 
Unions: 
No
 
Position Status: 
Exempt
 
Weekly Hours: 
40
 
Employee Status: 
Regular
 Sutter Health is an equal opportunity employerEOE/M/F/Disability/Veterans.
 
Pay Range is $168,958.40 to $270,337.60 / annual salary
 The compensation range may vary based on the geographic location where the position is filled. Total compensation considers multiple factors, including, but not limited to a candidate’s experience, education, skills, licensure, certifications, departmental equity, training, and organizational needs. Base pay is only one component of Sutter Health’s comprehensive total rewards program. Eligible positions also include a comprehensive benefits package.

AI Engineer

Mercor

<div>
 
 About Mercor
 
 
 
 Mercor is at the intersection of labor markets and AI research. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development.
 
 
 Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day.
 
 
 Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You&rsquo;ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society.
 
 
 Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our new San Francisco headquarters.
 
 
 
 About The Role
 
 
 
 As a Machine Learning Engineer at Mercor, you&rsquo;ll operate at the intersection of backend engineering and applied machine learning. ML Engineers at Mercor are generalists first, shipping production systems that power performance prediction, search, recommendation, and fraud detection while also bringing statistical and modeling rigor where it matters. The work spans everything from building APIs and infrastructure to training and deploying models, always tied closely to core product outcomes. You&rsquo;ll collaborate with product engineers and operations to deliver systems that directly impact how companies source talent and how candidates find opportunities
 
 
 
 You Will
 
 
 
 <ul>
 <li>
 Research, train, and productionize ML models for engagement prediction, scoring, search
 </li>
 <li>
 Build backend infrastructure and APIs to serve ML models reliably at scale.
 </li>
 <li>
 Run experiments, analyze results, and iterate quickly to improve both models and product performance.
 </li>
 <li>
 Work cross-functionally with Operations and Product to translate business needs into model-driven solutions.
 </li>
 <li>
 Wear many hats: from backend engineer to applied ML practitioner to product problem-solver.
 
 
 </li>
 </ul>
 
 What We&rsquo;re Looking For
 
 
 
 <ul>
 <li>
 Strong backend engineering skills (ex. Python/Django or similar) plus a solid foundation in applied ML and statistics.
 </li>
 <li>
 Proven experience shipping production systems or ML-driven products end-to-end.
 </li>
 <li>
 High ownership and comfort operating in ambiguous, fast-changing environments.
 </li>
 <li>
 Generalist mindset: willing to flex between backend, modeling, data pipelines, and product problem-solving.
 
 
 </li>
 </ul>
 
 Why Mercor
 
 
 
 <ul>
 <li>
 Impact: Your work powers how the world&rsquo;s leading AI labs train and test their models.
 </li>
 <li>
 Learning: Get early insights into frontier model capabilities months before the market.
 </li>
 <li>
 Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Generous equity grant vested over 4 years
 </li>
 <li>
 A $20K relocation bonus (if moving to the Bay Area)
 </li>
 <li>
 A $10K housing bonus (if you live within 0.5 miles of our office)
 </li>
 <li>
 A $1K monthly stipend for meals
 </li>
 <li>
 Free Equinox membership
 </li>
 <li>
 Health insurance
 
 
 </li>
 </ul>
 Compensation Range: $130K - $500K
 
 
</div>

Machine Learning Engineer

Explore 186+ Ml ai engineering intern in San Francisco this March. Find roles across top companies and apply now to boost your career.