machine learning engineer

Si-Ware Systems

<div>
 Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
 
 
 At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
 
 
 As a
 
 Machine Learning Engineer
 
 at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
 
 
 You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
 
 
 initiatives.
 
 
 
 Responsibilities:
 
 
 
 
 Machine Learning &amp; Modeling
 
 
 
 <ul>
 <li>
 Perform data cleaning, preprocessing, and transformation for training and evaluation.
 </li>
 <li>
 Contribute to the development, training, and improvement of machine learning models.
 </li>
 <li>
 Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).
 </li>
 <li>
 Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).
 </li>
 <li>
 Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.
 </li>
 <li>
 Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.
 </li>
 <li>
 Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.
 
 
 
 </li>
 </ul>
 
 Production &amp; System Integration
 
 
 
 <ul>
 <li>
 Design and implement production-ready ML pipelines integrated with software applications and hardware systems.
 </li>
 <li>
 Ensure models are maintainable, version-controlled, and deployable within real-world products.
 </li>
 <li>
 Contribute to the design and evolution of ML system architecture and reusable pipeline components.
 </li>
 <li>
 Develop unit and integration tests for ML components to ensure reliability.
 </li>
 <li>
 Document ML modules and interfaces to support long-term maintainability.
 </li>
 <li>
 Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.
 
 
 
 </li>
 </ul>
 
 Tools, Innovation &amp; Growth
 
 
 
 <ul>
 <li>
 Participate in the development of internal and customer-facing ML-driven tools and automation utilities.
 </li>
 <li>
 Stay updated with the latest machine learning research, tools, and industry practices.
 </li>
 <li>
 Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.
 </li>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Engineering, Mathematics, or a related field.
 </li>
 <li>
 1 - 4 years of relevant industry or applied ML experience preferred.
 </li>
 <li>
 Strong focus on applied machine learning and engineering implementation.
 </li>
 <li>
 Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).
 </li>
 <li>
 Familiarity with at least one deep learning framework (PyTorch or TensorFlow).
 </li>
 <li>
 Solid understanding of software engineering principles (modular design, version control, testing, debugging).
 </li>
 <li>
 Experience structuring modular Python codebases.
 </li>
 <li>
 Familiarity with packaging, model serialization, and reproducible pipelines.
 </li>
 <li>
 Experience working within larger software systems (not only notebooks).
 </li>
 <li>
 Strong analytical and problem-solving skills.
 </li>
 <li>
 Good communication skills and ability to work in a collaborative team environment.
 </li>
 <li>
 Proficiency in English (reading and writing).
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).
 </li>
 <li>
 Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).
 </li>
 <li>
 Experience contributing to ML tools, internal platforms, or data analysis software.
 </li>
 <li>
 Knowledge of MLOps practices (Git, CI/CD, Docker).
 </li>
 <li>
 Experience with cloud platforms (AWS, GCP, Azure).
 </li>
 <li>
 Experience with robotics frameworks (ROS).
 </li>
 <li>
 Experience with sensor fusion.
 </li>
 <li>
 Experience with real-time ML inference.
 </li>
 <li>
 Basic understanding of control systems.
 </li>
 <li>
 Exposure to data visualization or BI tools.
 </li>
 <li>
 Contributions to Kaggle competitions, research projects, or open-source initiatives.
 </li>
 </ul>
</div>

Machine Learning Engineer

Si-ware Systems

<div>Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
As a Machine Learning Engineer at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
initiatives.<h4 class="jobSectionHeader">
Responsibilities:</h4>
Machine Learning &amp; Modeling<ul><li>
Perform data cleaning, preprocessing, and transformation for training and evaluation.</li></ul><ul><li>
Contribute to the development, training, and improvement of machine learning models.</li><li>
Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).</li><li>
Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).</li><li>
Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.</li><li>
Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.</li><li>
Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.</li></ul>
Production &amp; System Integration<ul><li>
Design and implement production-ready ML pipelines integrated with software applications and hardware systems.</li><li>
Ensure models are maintainable, version-controlled, and deployable within real-world products.</li><li>
Contribute to the design and evolution of ML system architecture and reusable pipeline components.</li><li>
Develop unit and integration tests for ML components to ensure reliability.</li><li>
Document ML modules and interfaces to support long-term maintainability.</li><li>
Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.</li></ul>
Tools, Innovation &amp; Growth<ul><li>
Participate in the development of internal and customer-facing ML-driven tools and automation utilities.</li><li>
Stay updated with the latest machine learning research, tools, and industry practices.</li><li>
Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.</li></ul><ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering, Mathematics, or a related field.</li><li>
1 - 4 years of relevant industry or applied ML experience preferred.</li><li>
Strong focus on applied machine learning and engineering implementation.</li><li>
Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).</li><li>
Familiarity with at least one deep learning framework (PyTorch or TensorFlow).</li><li>
Solid understanding of software engineering principles (modular design, version control, testing, debugging).</li><li>
Experience structuring modular Python codebases.</li><li>
Familiarity with packaging, model serialization, and reproducible pipelines.</li><li>
Experience working within larger software systems (not only notebooks).</li><li>
Strong analytical and problem-solving skills.</li><li>
Good communication skills and ability to work in a collaborative team environment.</li><li>
Proficiency in English (reading and writing).</li></ul><h4 class="jobSectionHeader">
Nice to Have:</h4><ul><li>
Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).</li><li>
Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).</li><li>
Experience contributing to ML tools, internal platforms, or data analysis software.</li><li>
Knowledge of MLOps practices (Git, CI/CD, Docker).</li><li>
Experience with cloud platforms (AWS, GCP, Azure).</li><li>
Experience with robotics frameworks (ROS).</li><li>
Experience with sensor fusion.</li><li>
Experience with real-time ML inference.</li><li>
Basic understanding of control systems.</li><li>
Exposure to data visualization or BI tools.</li><li>
Contributions to Kaggle competitions, research projects, or open-source initiatives.</li></ul></div>

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>

AMA CONSULTING

AMA Consulting, LLC, a Maryland-based Limited Liability Company, provides analytical, acquisition, and business management support services to federal agencies. AMA Consulting is an Equal Opportunity/Affirmative Action Employer. Join a dynamic team of forward-thinking professionals in our growing company supporting cutting-edge AI innovation and national security initiatives.
 
Machine Learning Engineer
 
The Machine Learning Engineer will develop the AI testing, evaluation, and monitoring tools that power AMA's AI assurance platform. This role focuses on building model evaluation pipelines, bias detection systems, and AI monitoring capabilities.
 
Key responsibilities include: 
<ul>
<li>AI Model Testing</li>
<li>Develop automated pipelines for testing AI model performance.</li>
<li>Build model validation systems and evaluation metrics.</li>
<li>Implement bias detection and fairness testing tools.</li>
<li>AI Monitoring Systems</li>
<li>Develop tools to monitor AI models in production environments.</li>
<li>Build explainability and model transparency capabilities.</li>
<li>Data Science & AI Development</li>
<li>Build machine learning models supporting platform analytics.</li>
<li>Develop algorithms supporting AI assurance and risk evaluation.</li>
<li>Platform Integration</li>
<li>Work closely with software engineers to integrate ML tools into the platform infrastructure.</li>
</ul>
Required qualifications to be successful in this role: 
<ul>
<li>Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field.</li>
<li>5+ years of experience in machine learning engineering.</li>
<li>Strong proficiency in Python and machine learning libraries.</li>
<li>Experience building production ML pipelines.</li>
<li>Experience with explainable AI frameworks.</li>
</ul>
Preferred skills include: 
<ul>
<li>Familiarity with bias detection and model fairness techniques.</li>
<li>Experience supporting federal or defense programs.</li>
</ul>

ShrinQ Consulting Group Inc

<div>
 
 
 Role Overview
 
 
 
 We are seeking a Machine Learning Engineer to design, develop, and deploy machine learning models that support data-driven decision-making and intelligent applications. The role involves working with data scientists, engineers, and product teams to build scalable machine learning pipelines and integrate models into production systems.
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Develop, train, and optimize machine learning models for real-world applications
 </li>
 <li>
 Build and maintain data pipelines for model training and evaluation
 </li>
 <li>
 Deploy machine learning models into production environments
 </li>
 <li>
 Work with large datasets to extract insights and improve model performance
 </li>
 <li>
 Collaborate with data scientists and software engineers to integrate ML solutions into products
 </li>
 <li>
 Monitor model performance and retrain models as needed
 </li>
 <li>
 Implement best practices for model versioning, testing, and deployment
 </li>
 </ul>
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Artificial Intelligence, or related field
 </li>
 <li>
 Strong programming skills in Python or similar languages
 </li>
 <li>
 Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
 </li>
 <li>
 Understanding of statistics, data structures, and algorithms
 </li>
 <li>
 Experience with data preprocessing and feature engineering
 </li>
 <li>
 Knowledge of SQL and working with large datasets
 </li>
 </ul>
 
 
 Preferred Skills
 
 
 <ul>
 <li>
 Experience with cloud platforms (AWS, Azure, or GCP)
 </li>
 <li>
 Familiarity with MLOps tools and model deployment pipelines
 </li>
 <li>
 Knowledge of big data technologies like Spark or Hadoop
 </li>
 <li>
 Experience with deep learning and natural language processing (NLP)
 </li>
 </ul>
 
 
</div>

Apple

Apple is where individual imaginations converge, united by values that drive exceptional innovation. Every product we develop and service we deliver emerges from our collective commitment to strengthening each other's ideas. This collaborative spirit stems from a shared conviction that we can create something extraordinary and deliver it to the world, transforming lives for the better. The diversity of our people and their perspectives fuels the innovation that permeates everything we accomplish. When we embrace everyone's contributions, we achieve our finest work. Here, you will not simply join something; you will contribute something meaningful. 
 
Spatial computing represents a transformative frontier where digital experiences merge seamlessly with physical environments. Through advanced sensor fusion, edge computing architectures, and efficient on-device models, we are redefining how people interact with technology in their everyday lives. This evolution enables entirely new possibilities while maintaining genuine human connection. 
 
Our computer vision and machine learning (CVML) team of researchers and engineers tackle previously unsolved technical challenges at the intersection of perception, intelligence, and real-time processing. We seek a driven and dedicated engineer with deep expertise in machine learning systems. As a member of our dynamic team, you will have the unique and rewarding opportunity to influence next-generation products that will captivate and inspire millions of people daily. 
 
Are you prepared to make a lasting impact on the future of technology? 
 
Description 
 
We seek a proactive CVML engineer to join our team in designing and implementing advanced systems for spatial computing and artificial intelligence. The ideal candidate demonstrates passion for translating cutting-edge research into production-ready solutions that delight users. 
 
In this role, you will collaborate closely with computer vision and machine learning researchers to develop world-class algorithms that advance the state of the art while creating deeply personal experiences for our users. You will drive the development of a unified context representation and a scalable, device-agnostic framework. You will help establish efficient mechanisms for managing dynamic and persistent contextual insights. This work will enable intelligent, personalized experiences through efficient management of both real-time and historical contextual data, supporting advanced AI and machine learning models. 
 
Your work will span the full spectrum from applying breakthrough research to solving real-world problems, optimizing model efficiency for on-device inference, and ensuring algorithms perform flawlessly in production. You will be instrumental in pushing the boundaries of what is possible when sophisticated machine learning meets spatial computing.&quot;,&quot;responsibilities&quot;:&quot;Developing cutting-edge AI systems that push the boundaries of what is possible. 
 
Collaborating cross-functionally to understand algorithmic constraints and dependencies. 
 
Working with engineers and designers to develop quick proof-of-concepts with novel hardware for innovative applications. 
 
Cooperating with your team members to prepare presentations, papers, and talks to explain your inventions. 
 
Advising on feature definitions/promising development directions in cross-functional groups. 
 
Preferred Qualifications 
 
M.S., or Ph.D. and 5+ years of industry experience in machine learning or related fields. 
 
Excellent math skills, including linear algebra, computational science and optimization. 
 
Creativity and curiosity for solving highly complex problems. 
 
Excellent communication and collaboration skills. 
 
An interest in incubating ideas and taking them to finish. 
 
Experience shipping algorithms or publishing in top tier CVML conferences. 
 
Minimum Qualifications 
 
B.S and 5+ years of experience in software engineering, computer vision, machine learning or related fields. 
 
Experience in deep learning frameworks such as PyTorch. 
 
Experience in C++ and Swift. 
 
Strong experience in Python, including popular science libraries like Numpy, Pandas, etc. 
 
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 $181,100 and $318,400, 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.

Baselayer

<div>
 
 
 About Baselayer:
 
 
 
 Trusted by 2,200+ financial institutions, Baselayer is the intelligent business identity platform that helps verify any business, automate KYB, and monitor real-time risk. Baselayer&rsquo;s B2B risk solutions &amp; identity graph network leverage state &amp; federal government filings and proprietary data sources to prevent fraud, accelerate onboarding, and lower credit losses.&nbsp;
 
 
 &nbsp;
 
 
 
 About You:
 
 
 
 You want to learn from the best of the best, get your hands dirty, and put in the work to hit your full potential. You're not just doing it for the win&mdash;you're doing it because you have something to prove and want to be great. You are looking to be an impeccable machine learning engineer working on cutting-edge AI solutions.
 
 <ul>
 <li>
 You have 4-8 years of experience in machine learning development, working with Python and building ML models
 </li>
 <li>
 You're comfortable working with large-scale data and enjoy optimizing performance for computationally intensive ML systems
 </li>
 <li>
 You have a strong foundation in AI/ML fundamentals, particularly with LLMs, and are eager to experiment with emerging techniques
 </li>
 <li>
 You prioritize responsible AI practices and model governance, especially in regulated environments like KYC/KYB
 </li>
 <li>
 You have a keen eye for detail and take pride in writing clean, maintainable code while optimizing for model performance
 </li>
 <li>
 You thrive in a high-trust, ownership-focused environment and are comfortable working across different levels of abstraction
 </li>
 <li>
 Problem-solver who navigates the unknown confidently
 </li>
 <li>
 Proactive self-starter who thrives in dynamic settings
 </li>
 <li>
 Incredibly intelligent and clever. You take pride in your models
 </li>
 <li>
 Highly feedback-oriented. We believe in radical candor and using feedback to get to the next level
 </li>
 </ul>
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Model Development &amp; Integration: Build and maintain ML models and integrate them with various data sources, ensuring scalability, high performance, and adaptability for autonomous agents in the GTM space
 </li>
 <li>
 ML System Design: Architect and design core ML services that support KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases
 </li>
 <li>
 Data Processing &amp; Feature Engineering: Develop and maintain robust data pipelines for feature extraction and transformation, focusing on scalability and performance when handling large-scale, high-dimensional data
 </li>
 <li>
 Advanced ML Techniques: Implement and experiment with state-of-the-art techniques including reinforcement learning from human feedback (RLHF) and parameter-efficient fine-tuning methods (e.g., LoRA) to improve LLMs for specific use cases within the identity space
 </li>
 <li>
 ML Infrastructure: Build and maintain infrastructure for model training, evaluation, and deployment, creating a scalable platform foundation for continued innovation
 </li>
 <li>
 Model Governance &amp; Compliance: Ensure ML systems meet industry standards for fairness, explainability, and compliance, particularly around KYC/KYB regulations
 </li>
 <li>
 Performance Optimization: Implement optimizations for model inference and training, ensuring ML services can efficiently process identity data while maintaining reliability
 </li>
 <li>
 Experimentation &amp; Evaluation: Design and conduct experiments to evaluate model performance, debug issues, and monitor ML services, while continuously improving architectures to handle diverse data and use cases
 </li>
 </ul>
 
 &nbsp;
 
 
 
 Work Location
 
 
 <ul>
 <li>
 Hybrid in SF. In office 3 days/week
 </li>
 </ul>
 
 &nbsp;
 
 
 &nbsp;
 
 
 
 Compensation and Benefits
 
 
 <ul>
 <li>
 
 Salary range of $145,000 to $225,000
 
 </li>
 <li>
 
 Equity package
 
 </li>
 <li>
 
 Unlimited vacation
 
 </li>
 <li>
 
 Fully paid health insurance, dental, and vision
 
 </li>
 <li>
 
 401(k) with company match
 
 </li>
 </ul>
 
 &nbsp;
 
</div>

Search 10000+ Machine learning engineer jobs near you this March. New roles added regularly across leading industries. Apply today.

10000 machine learning engineer jobs Near Me

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer