senior machine learning engineer

Information Technology Senior Management Forum

<div><div>Posted Date
2/27/2026
</div><div>Description
<div>Senior Machine Learning Engineer
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
What you&rsquo;ll do in the role:
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:<ul><li>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</li><li>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</li><li>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</li><li>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</li><li>
Retrain, maintain, and monitor models in production.</li><li>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>
Construct optimized data pipelines to feed ML models.</li><li>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</li><li>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</li><li>
Use programming languages like Python, Scala, or Java.</li></ul>
Basic Qualifications:<ul><li>
Bachelor&rsquo;s Degree</li><li>
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)</li><li>
At least 3 years of experience designing and building data-intensive solutions using distributed computing</li><li>
At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)</li><li>
At least 1 year of experience productionizing, monitoring, and maintaining models</li></ul>
Preferred Qualifications:<ul><li>
1+ years of experience building, scaling, and optimizing ML systems</li><li>
1+ years of experience with data gathering and preparation for ML models</li><li>
2+ years of experience developing performant, resilient, and maintainable code</li><li>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field</li><li>
3+ years of experience with distributed file systems or multi-node database paradigms</li><li>
Contributed to open source ML software</li><li>
Authored/co-authored a paper on a ML technique, model, or proof of concept</li><li>
3+ years of experience building production-ready data pipelines that feed ML models</li><li>
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</li></ul>
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $161,800 - $184,600 for Senior Machine Learning Engineer 
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Senior Machine Learning Engineer
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
What you&rsquo;ll do in the role:
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:<ul><li>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</li><li>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</li><li>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</li><li>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</li><li>
Retrain, maintain, and monitor models in production.</li><li>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>
Construct optimized data pipelines to feed ML models.</li><li>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</li><li>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</li><li>
Use programming languages like Python, Scala, or Java.</li></ul>
Basic Qualifications:<ul><li>
Bachelor&rsquo;s Degree</li><li>
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)</li><li>
At least 3 years of experience designing and building data-intensive solutions using distributed computing</li><li>
At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)</li><li>
At least 1 year of experience productionizing, monitoring, and maintaining models</li></ul>
Preferred Qualifications:<ul><li>
1+ years of experience building, scaling, and optimizing ML systems</li><li>
1+ years of experience with data gathering and preparation for ML models</li><li>
2+ years of experience developing performant, resilient, and maintainable code</li><li>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field</li><li>
3+ years of experience with distributed file systems or multi-node database paradigms</li><li>
Contributed to open source ML software</li><li>
Authored/co-authored a paper on a ML technique, model, or proof of concept</li><li>
3+ years of experience building production-ready data pipelines that feed ML models</li><li>
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</li></ul>
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $161,800 - $184,600 for Senior Machine Learning Engineer 
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</div>
</div><div>Salary
161,800.00 - 184,600.00 Annual
</div><div></div><div><div>Type<div>
Full-time</div></div></div></div>

Senior Machine Learning Engineer

Unifonic

<div>Proudly voted a Great Place to Work&reg;, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies. 
We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we&rsquo;re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big! 
Meet the team!
Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic&rsquo;s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies. 
Help us shape the future of communication by:<ul><li>
Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.</li><li>
Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.</li><li>
Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.</li><li>
Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).</li><li>
Mentoring junior team members on the team, sharing knowledge, and advising the best machine learning and software engineering practices and approaches.</li><li>
Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.</li><li>
Developing and optimizing highly confident machine learning algorithms and models and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.</li><li>
Staying up to date with the latest machine learning research papers, and AI trends (i.e. Generative AI).</li><li>
Collaborating with the data engineering team and other teams to collect and analyze extensive datasets, extracting insights and patterns, in real-time, near-real-time, or batch processing mode.</li><li>
Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.</li><li>
Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.</li><li>
Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.</li><li>
Assisting in the project documentation and demos.</li></ul> 
What you'll bring:<ul><li>
Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.</li><li>
Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.</li><li>
Deep expertise in various NLP techniques and models, including but not limited to:Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)Large Language Models (LLMs) and their fine-tuning/adaptationVector embeddings and similarity searchText classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.</li><li>
Hands-on 3-5 years of relevant work experience as a Machine Learning Engineer.</li><li>
Hands-on 3+ years of experience with Python.</li><li>
Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.</li><li>
End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.</li><li>
Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling</li><li>
Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.</li><li>
Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.</li><li>
Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.</li><li>
Basic knowledge of Kubernetes and Docker is nice to have.</li><li>
Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.</li><li>
Experience with the Hugging Face libraries (i.e. transformers).</li><li>
Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.</li><li>
Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.</li><li>
Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.</li><li>
Familiar with Agile methodologies i.e. scrum and kanban.</li><li>
Ability to develop high-level architecture and low-level design, End-to-end for a specific project.</li><li>
Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is a plus.</li><li>
Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.</li><li>
Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.</li><li>
General knowledge of Data warehouse tools e.g. Vertica is a plus.</li><li>
A Bachelor&rsquo;s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).</li><li>
Excellent communication and collaboration skills.</li><li>
Good level of spoken and written Arabic and English.</li></ul>
As a Unifone you will receive a range of benefits:<ul><li>
Competitive salary and bonus.</li><li>
Unifonic share scheme (we are all owners!).</li><li>
30 holiday days after the first anniversary.</li><li>
Your Birthday off!</li><li>
Spend up to 25 days per year working from anywhere in the world!</li><li>
Paid leave for new parents.</li><li>
LinkedIn learning license.</li></ul></div>

Microsoft

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

Arrow Electronics

<div>
 
 Position
 
 
 
 Data Scientist / Senior Machine Learning Engineer
 
 
 
 Job Description
 
 
 
 
 Position Overview:
 
 
 
 As Data Scientist / Senior Machine Learning Engineer at Arrow Electronics, you will assume a pivotal role in driving our data science and machine learning initiatives to reach unprecedented heights. Collaborating closely with cross-functional multinational teams, your responsibilities will encompass the end-to-end machine learning lifecycle, including stakeholder communications, data analysis, designing, development, deployment, and optimization of cutting-edge models aimed at addressing intricate challenges and generating actionable insights. Your expertise in data analysis, model development, and deployment will be crucial in shaping the future of our company.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Machine Learning Model Development: Design, develop, and implement advanced machine learning models and algorithms to solve real-world problems in various domains.
 </li>
 <li>
 Data Analysis: Explore and analyze large datasets to extract meaningful insights and identify trends, anomalies, and patterns.
 </li>
 <li>
 Model Evaluation: Evaluate the performance of machine learning models using appropriate metrics and fine-tune models for optimal results.
 </li>
 <li>
 Deployment: Lead the deployment of machine learning models into production environments and ensure scalability and efficiency.
 </li>
 <li>
 Cross-functional Collaboration: Collaborate closely with business stakeholders, software engineers, data engineers, and domain experts to integrate machine learning solutions into our products and services.
 </li>
 <li>
 Mentorship: Provide guidance and mentorship to junior data scientists and machine learning engineers, fostering a culture of continuous learning and growth.
 </li>
 <li>
 Research and Innovation: Stay up-to-date with the latest advancements in the field of data science and machine learning and apply innovative techniques to solve complex challenges.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Proven experience (2 to 5 years) in developing and deploying machine learning models in a production environment.
 </li>
 <li>
 Proficiency in programming languages, especially Python, and experience with data-related libraries such as pandas, matplotlib, NumPy, etc.
 </li>
 <li>
 Strong knowledge of machine learning frameworks and libraries.
 </li>
 <li>
 Expertise in data preprocessing, feature engineering, and model selection.
 </li>
 <li>
 Familiarity with large language models and AI agent design.
 </li>
 <li>
 Experience with text-based NLP, information extraction, entity recognition, and text-related data science tasks is highly desirable.
 </li>
 <li>
 Strong communication skills and the ability to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience with vectorized databases and unstructured search engines is a plus.
 </li>
 <li>
 A Master&rsquo;s degree in computer science, Statistics, Data Science, or a related field is a plus.
 </li>
 <li>
 Publications or contributions to open-source projects are a plus.
 
 
 </li>
 </ul>
 If you are a passionate and experienced Data Scientist / Senior Machine Learning Engineer who thrives on solving challenging problems and wants to make a significant impact in the field, we encourage you to apply and join our dynamic team at Arrow Electronics Together, we will drive innovation and shape the future of data science and machine learning.
 
 
 
 Location:
 
 
 
 EG-Cairo, Egypt (Al Emdad &amp; Al Tamween)
 
 
 
 Time Type
 
 
 
 Full time
 
 
 
 Job Category
 
 
 
 Information Technology
</div>

Data Scientist / Senior Machine Learning Engineer

Zendesk

<div><h2 class="jobSectionHeader">Job Description
</h2>Role: Machine Learning Engineer
TBH#:
Location: US 
 
Zendesk&rsquo;s people have one goal in mind: to make Customer Experience better. 
 
The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning, deep learning, and large language models (LLMs) to create highly impactful outcomes. Our commitment is to advance the state of AI, statistical modeling, and robust system design to enhance and expand our core business capabilities.
<h3 class="jobSectionHeader">Role Overview
</h3>As a Machine Learning Engineer, you will serve as a technical and strategic member within the team, driving the development and deployment of advanced data science and machine learning solutions&mdash;particularly those harnessing LLMs and deep learning. You will architect and scale ML systems, foster effective cross-functional collaborations, and ensure that business value is embedded in every technical decision. Your business acumen allows you to translate complex analytical approaches into actionable insights and stakeholder-friendly narratives, strengthening partnership and adoption across the enterprise.
<h3 class="jobSectionHeader">Key Responsibilities
</h3><ul><li>Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.
</li><li>Build scalable, robust data science systems&mdash;from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring&mdash;meeting enterprise-grade performance, reliability, and compliance standards.
</li><li>Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.
</li><li>Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.
</li><li>Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.
</li><li>Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.
</li><li>Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).
</li><li>Fostering technical excellence and ensuring alignment with business objectives.
</li></ul><h3 class="jobSectionHeader">What We&rsquo;re Looking For
</h3><ul><li>Education &amp; Experience:
<ul><li>3+ years&rsquo; experience in Data Science, Machine Learning, or a related field
</li><li>BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)
</li></ul></li><li>Technical Expertise:
<ul><li>Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)
</li><li>Strong programming skills (Python preferred; Java, Scala, or similar also valued)
</li><li>Proven ability to build and optimize scalable data science solutions&mdash;end-to-end&mdash;from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)
</li><li>Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)
</li></ul></li><li>Business Acumen:
<ul><li>Demonstrated success in translating business needs into analytical solutions, driving quantifiable impact
</li><li>Strong stakeholder engagement skills, with a track record of building trusted business partnerships and driving adoption of data science initiatives
</li></ul></li><li>Communication &amp; Collaboration:
<ul><li>Exceptional ability to simplify and communicate complex data science concepts to technical and non-technical audiences alike
</li><li>Experience working cross-functionally with engineers, analysts, and product leaders
</li><li>Steadfast commitment to continuous learning, collaboration, and fostering an inclusive, innovative team environment
</li></ul></li></ul><h3 class="jobSectionHeader">Why You&rsquo;ll Thrive Here
</h3><ul><li>Opportunity to develop and scale of LLM and deep learning solutions with real-world business impact
</li><li>An environment that values innovation, ownership, and professional growth
</li><li>The chance to work on high-visibility, high-impact projects at scale alongside a passionate multidisciplinary team 
</li></ul> 
 
Zendesk builds software for better customer relationships. It empowers organizations to improve customer engagement and better understand their customers. Zendesk products are easy to use and implement. They give organizations the flexibility to move quickly, focus on innovation, and scale with their growth. Based in San Francisco, Zendesk has operations in the United States, Europe, Asia, Australia, and South America. Learn more at www.zendesk.com . 
 
Individuals seeking employment at Zendesk are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, or sexual orientation.
The US annualized base salary range for this position is $206,000.00-$308,000.00. This position may also be eligible for bonus, benefits, or related incentives. While this range reflects the minimum and maximum value for new hire salaries for the position across all US locations, the offer for the successful candidate for this position will be based on job related capabilities, applicable experience, and other factors such as work location. Please note that the compensation details listed in US role postings reflect the base salary only (or OTE for commissions based roles), and do not include bonus, benefits, or related incentives.
Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in-office schedule is to be determined by the hiring manager.
The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we&rsquo;re proud of our ongoing efforts to foster global diversity, equity, &amp; inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here .
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.</div>

Linvex Solutions

Company: LinvexLocation: Bahria Town, Lahore (On-Site)Employment Type: Full-TimeApply at: LinvexAi@gmail.com⸻Role OverviewWe are seeking a Senior Machine Learning Engineer with strong backend expertise to lead the development of intelligent edge-to-cloud AI systems powering VeluraSense.⸻Key Responsibilities<ul><li>Design and develop ML models for:</li></ul><ul><li>Fall detection</li></ul><ul><li>Activity recognition</li></ul><ul><li>Respiratory and heart rate estimation</li></ul><ul><li>Anomaly detection in time-series health signals</li></ul><ul><li>Process and analyze Wi-Fi CSI and mmWave radar signal data</li></ul><ul><li>Build real-time inference pipelines on edge devices</li></ul><ul><li>Architect and maintain scalable cloud-based ML systems</li></ul><ul><li>Develop backend APIs for health insights and alert systems</li></ul><ul><li>Optimize models for latency, efficiency, and deployment constraints</li></ul><ul><li>Implement model monitoring, validation, and drift detection</li></ul><ul><li>Collaborate with hardware and embedded teams</li></ul><ul><li>Ensure privacy-by-design architecture and secure data handling</li></ul>⸻Required Experience (4+ Years)Machine Learning &amp; Signal Processing<ul><li>Strong experience in Python</li></ul><ul><li>Deep understanding of time-series analysis and signal processing</li></ul><ul><li>Experience with PyTorch, TensorFlow, or similar frameworks</li></ul><ul><li>Experience deploying models into production</li></ul><ul><li>Experience with anomaly detection systems</li></ul>Backend &amp; Systems<ul><li>Strong experience building REST APIs (FastAPI, Django, or similar)</li></ul><ul><li>Experience designing scalable microservices</li></ul><ul><li>Experience with PostgreSQL or other relational databases</li></ul><ul><li>Experience with Docker and production deployments</li></ul><ul><li>Familiarity with cloud platforms (AWS, GCP, or Azure)</li></ul>⸻Preferred Qualifications<ul><li>Experience with edge AI deployment</li></ul><ul><li>Knowledge of radar signal processing or RF-based sensing</li></ul><ul><li>Experience with real-time systems</li></ul><ul><li>Familiarity with IoT architectures</li></ul><ul><li>Experience working in regulated domains (health-tech, medical devices)</li></ul><ul><li>Understanding of data privacy and compliance standards</li></ul>⸻Ideal Candidate<ul><li>Comfortable owning architecture decisions</li></ul><ul><li>Strong problem-solving and research mindset</li></ul><ul><li>Able to move from research prototype to production system</li></ul><ul><li>Interested in building privacy-first, life-impacting AI systems</li></ul><ul><li>Experience mentoring junior engineers is a plus</li></ul>⸻How to ApplySend your CV and GitHub or portfolio to:LinvexAi@gmail.comJob Type: Full-timeWork Location: In person

Senior Machine Learning Engineer (Edge AI & Backend Systems)

Code Graphers

Key Responsibilities:<ul><li>Advanced Model Development:</li><li>Design and implement complex models using machine learning and deep learning, particularly focusing on natural language processing (NLP), named entity recognition (NER), and large language models (LLMs) such as BERT.</li><li>Create complex AI agent using Langchain, llamaIndex, auto-gen</li><li>Create Retrival Augmented Generation (RAG) Applications</li><li>Multi-agent workflows and bots</li><li>Deep Learning and Neural Network Expertise</li><li>Apply a deep theoretical understanding of neural networks to develop sophisticated models that are both efficient and scalable.</li><li>Cross-Domain Integration</li><li>Incorporate knowledge of image processing to integrate multi-modal systems that leverage both text and visual data.</li><li>Research, Documentation, and Reporting</li><li>writing</li><li>Conduct ongoing research to stay ahead of new trends and techniques in machine learning, deep learning, and AI.</li><li>Creating custom APIs using frameworks such as FAST-API, Django, Flask</li><li>Have experince deploying ML models into production</li><li>Training LLMs with LoRA and QLoRA</li><li>experience with Quantization</li><li>Containerizing code via docker</li><li>Setting up MLOps pipelines with frameworks such as Data version control (DVC)</li><li>Creating Ci/CD pipelines</li><li>Image segmentation</li><li>Object detection</li><li>facial recognition</li></ul>Qualifications:<ul><li>Education: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Statistics, or a related field.</li><li>Experience:</li><li>4 - 6 years of experience in ML model development, including hands-on experience with NLP, NER, and LLMs.</li><li>Experience with neural network design and implementation.</li><li>Technical Skills:</li><li>Proficiency in Python, with good knowledge of ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).</li><li>Experience in developing and implementing models using BERT, RAG, LangChain, and other advanced ML techniques.</li><li>Familiarity with image processing techniques</li><li>Deep theoretical understanding of how models work, including underlying mathematical principles.</li><li>Document methodologies and innovations thoroughly; prepare detailed reports and presentations for technical and non-technical stakeholders.</li><li>Collaborate with cross-functional teams to ensure models are well-integrated into the company&rsquo;s product ecosystem.</li><li>Optimize neural network architectures and parameters for improved performance across various tasks.</li><li>Utilize techniques like Language Model Chaining and Retriever-Augmented Generation (RAG) to enhance text-based applications.</li></ul>Nice to have :<ul><li>experience with auto scaling</li><li>experience with GANs</li><li>experience with stable diffusion based models</li><li>experience with 3D neural networks</li><li>experience with Meta learning</li><li>worked on Raspberry PI or embedding systems and deployed ML model onto them before</li></ul>Job Type: Full-timeAbility to commute/relocate:<ul><li>Lahore: Reliably commute or planning to relocate before starting work (Required)</li></ul>Application Question(s):<ul><li>Did you Lead the Team before ?</li></ul>Experience:<ul><li>Machine Learning: 5 years (Required)</li></ul>Work Location: In person

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

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