machine engineer

Abaka AI

<div>
 
 About Abaka AI
 
 
 
 Abaka AI is built on one mission: to be the world&rsquo;s most trusted data partner for AI companies. More than 1,000 industry leaders across Generative AI, Embodied AI, and Automotive AI rely on us to power their data pipelines. With our headquarters in Silicon Valley&mdash;and teams in Paris, Singapore, and Tokyo&mdash;we support global partners with fast, reliable, and scalable data solutions.
 
 
 Our offerings include a diverse catalog of off-the-shelf datasets (image, video, multimodal, reasoning, 3D, and beyond) as well as comprehensive data collection and annotation services. Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides the foundation for building high-performance AI systems.
 
 
 
 About The Role
 
 
 
 We&rsquo;re hiring our first Machine Learning Engineer in the United States, a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own the design and development of scalable training pipelines, work directly with our data engineering and research teams, and help drive the technical roadmap for model development across multiple modalities.
 
 
 As an early member of the engineering team, you will influence core decisions around model training strategy, experimentation frameworks, distributed infrastructure, and internal best practices. Your work will directly impact the performance of frontier models trained on Abaka datasets and will help elevate the technical bar for our clients and partners.
 
 
 If you thrive in high-ownership environments and want to shape the machine learning foundation of a fast-moving AI company, this role offers an opportunity to make an immediate and lasting impact.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
 </li>
 <li>
 Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
 </li>
 <li>
 Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
 </li>
 <li>
 Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
 </li>
 <li>
 Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
 </li>
 <li>
 Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
 </li>
 <li>
 Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Strong academic background in computer science, artificial intelligence, machine learning, or related fields. Master&rsquo;s degree or Ph.D. is preferred.
 </li>
 <li>
 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
 </li>
 <li>
 Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
 </li>
 <li>
 Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
 </li>
 <li>
 Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
 </li>
 <li>
 Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
 </li>
 <li>
 Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
 </li>
 <li>
 Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.
 
 
 </li>
 </ul>
 
 Compensation &amp; Benefits
 
 
 
 The base salary range for this position is $175,000 - $275,000 USD annually.
 
 
 Compensation may vary outside of this range depending on a number of factors, including a candidate&rsquo;s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work at Abaka AI. This role is eligible for equity, as well as a comprehensive benefits package (health, dental, vision, PTO, flexible work schedule).
</div>

Machine Learning Engineer

PayPal

<div>
 
 The Company
 
 
 PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy.
 
 
 We operate a global, two-sided network at scale that connects hundreds of millions of merchants and consumers. We help merchants and consumers connect, transact, and complete payments, whether they are online or in person. PayPal is more than a connection to third-party payment networks. We provide proprietary payment solutions accepted by merchants that enable the completion of payments on our platform on behalf of our customers.
 
 
 We offer our customers the flexibility to use their accounts to purchase and receive payments for goods and services, as well as the ability to transfer and withdraw funds. We enable consumers to exchange funds more safely with merchants using a variety of funding sources, which may include a bank account, a PayPal or Venmo account balance, PayPal and Venmo branded credit products, a credit card, a debit card, certain cryptocurrencies, or other stored value products such as gift cards, and eligible credit card rewards. Our PayPal, Venmo, and Xoom products also make it safer and simpler for friends and family to transfer funds to each other. We offer merchants an end-to-end payments solution that provides authorization and settlement capabilities, as well as instant access to funds and payouts. We also help merchants connect with their customers, process exchanges and returns, and manage risk. We enable consumers to engage in cross-border shopping and merchants to extend their global reach while reducing the complexity and friction involved in enabling cross-border trade.
 
 
 Our beliefs are the foundation for how we conduct business every day. We live each day guided by our core values of Inclusion, Innovation, Collaboration, and Wellness. Together, our values ensure that we work together as one global team with our customers at the center of everything we do &ndash; and they push us to ensure we take care of ourselves, each other, and our communities.
 
 
 
 Job Summary:
 
 
 
 This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences.
 
 
 
 Job Description:
 
 
 
 
 Essential Responsibilities:
 
 
 
 <ul>
 <li>
 Assist in the development and optimization of machine learning models.
 </li>
 <li>
 Preprocess and analyze datasets to ensure data quality.
 </li>
 <li>
 Collaborate with senior engineers and data scientists on model deployment.
 </li>
 <li>
 Conduct experiments and run machine learning tests.
 </li>
 <li>
 Stay updated with the latest advancements in machine learning.
 
 
 </li>
 </ul>
 
 Minimum Qualifications:
 
 
 
 <ul>
 <li>
 1+ years relevant experience and a Bachelor&rsquo;s degree OR Any equivalent combination of education and experience.
 </li>
 <li>
 Familiarity with ML frameworks like TensorFlow or scikit-learn.
 </li>
 <li>
 Strong analytical and problem-solving skills.
 
 
 </li>
 </ul>
 
 Additional Responsibilities &amp; Preferred Qualifications:
 
 
 
 
 Role And Responsibilities
 
 
 <ul>
 <li>
 Support the lead of the team in performing oversight of high-impact statistical model and AI applications in a variety of business function areas, including but not limited to fraud detection, credit underwriting, marketing analytics etc.
 </li>
 <li>
 Conduct quantitative and qualitative model validation according to Model Risk Management Policy to identify and understand model risk issues
 </li>
 <li>
 Collaborate with business units and model developers to remediate model issues and provide subject-matter expert opinion on model improvements
 </li>
 <li>
 Perform model and AI risk governance related activities in line with enterprise risk framework, to ensure PayPal&rsquo;s AI applications are compliant with ever evolving regulatory expectation such as Responsible AI.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 This position requires the ability and curiosity to learn various advanced modeling methods/AI techniques, covering a broader business function. This role also requires candidate to have the capability in building effective relationship with various stakeholders including business owners, model owners, model developers and control officers. The candidate must possess excellent communication, writing and presentation skills.
 
 
 <ul>
 <li>
 An advanced degree in a quantitative field, such as statistics, mathematics, computer science or engineering essential
 </li>
 <li>
 Advanced knowledge of statistical and machine learning models (e.g., logistic regression, time series analysis, random forests, SVMs, XGBoost, CNNs/RNNs)
 </li>
 <li>
 Possessing advanced coding skills in dealing with big data (e.g., Scikit-learn in Python, Tensorflow, Hadoop, Spark , SQL, etc.)
 </li>
 <li>
 Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or marketing analytics obtained either in academic or financial industry
 </li>
 <li>
 Ability to work effectively both independently and in a team environment
 </li>
 <li>
 Ability to communicate effectively and establish constructive relationship with stakeholders
 
 
 </li>
 </ul>
 
 Subsidiary:
 
 
 
 PayPal
 
 
 
 Travel Percent:
 
 
 
 0
 
 
 The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is:
 
 
 
 Primary Location : Pay Range:
 
 
 
 Chicago, Illinois : ($117,500.00 - $174,350.00 Annually)
 
 
 
 Additional Location(s) : Pay Range:
 
 
 
 Austin, Texas : ($117,500.00 - $174,350.00 Annually)
 
 
 Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable.
 
 
 PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam. All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us.
 
 
 For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations.
 
 
 
 Our Benefits:
 
 
 
 At PayPal, we&rsquo;re committed to building an equitable and inclusive global economy. And we can&rsquo;t do this without our most important asset-you. That&rsquo;s why we offer comprehensive, choice-based programs, to support all aspects of personal wellbeing&mdash;physical, emotional, and financial&mdash;delivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health.
 
 
 
 Who We Are:
 
 
 
 Click Here to learn more about our culture and community.
 
 
 
 Commitment to Diversity and Inclusion
 
 
 
 PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at paypalglobaltalentacquisition@paypal.com.
 
 
 
 Belonging at PayPal:
 
 
 
 Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal.
 
 
 Any general requests for consideration of your skills, please Join our Talent Community.
 
 
 We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don&rsquo;t hesitate to apply.
</div>

Allocate

<div>
 
 About Allocate
 
 
 
 Allocate is transforming private market investing by enabling RIAs and family offices to seamlessly discover, model, and manage their private market exposure. Our platform combines curated fund and co-investment opportunities with institutional-grade infrastructure. Through a single, data-rich digital experience, clients access top-tier opportunities across venture capital, private equity, private credit, and other private asset classes&mdash;backed by powerful tracking, analytics, and administration tools.
 
 
 
 About The Role
 
 
 
 Allocate is looking to add an AI / Extraction Engineer to the team! There's a lot for us to build, and we need a strong engineer with a broad skillset who can jump right in to help us lay the technical foundation for the company's future.
 
 
 
 Essential Responsibilities And Duties
 
 
 
 <ul>
 <li>
 Build, train, and improve machine-learning models for production use.
 </li>
 <li>
 Develop evaluation and feedback loops that measurably improve model performance over time.
 </li>
 <li>
 Stand up the tooling to measure, monitor, and track model quality.
 </li>
 <li>
 Evaluate new models and techniques, and bring the best into production.
 </li>
 <li>
 Collaborate cross-functionally to ship ML-powered capabilities.
 
 
 </li>
 </ul>
 
 
 Must Have Experience
 
 
 
 
 <ul>
 <li>
 4+ years of applied ML / LLM engineering in production, with strong Python.
 </li>
 <li>
 Experience taking models from prototype to reliable production systems.
 </li>
 <li>
 Hands-on model fine-tuning and rigorous evaluation discipline.
 </li>
 <li>
 Proficiency with Git and version control systems.
 </li>
 <li>
 Hands-on experience with AI-assisted development tools such as Claude Code, OpenAI's Codex, or Cursor, and a demonstrated ability to incorporate new tools as they emerge.
 
 
 </li>
 </ul>
 
 
 Nice to Haves
 
 
 
 
 <ul>
 <li>
 Applied NLP, computer vision, or multimodal models.
 </li>
 <li>
 MLOps and model-monitoring tooling.
 </li>
 <li>
 Background in fintech or financial data.
 
 
 </li>
 </ul>
 
 
 Education
 
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience.
 
 
 </li>
 </ul>
 
 
 Essential Values &amp; Culture
 
 
 
 
 <ul>
 <li>
 Providing our clients with a world-class experience is our number one priority. We obsessively search for ways to improve the experience for our clients and partners&mdash;extraordinary response times, proactivity, and a top-tier experience in everything from product strategy to offline communications.
 </li>
 <li>
 Challenge convention: Instead of detailing all the reasons an idea may not work, we question things to determine how a viable idea may be put into motion.
 </li>
 <li>
 Commitment to continuous improvement: We find ways to personally scale each day by pushing ourselves up the learning curve.
 </li>
 <li>
 Meritocracy, not politics: We place the utmost value on results and reward through merit, not political agendas.
 </li>
 <li>
 Civil Discourse is embraced: Open, intellectually curious conversations are required to consistently arrive at the best decisions. Respect is paramount, but the mission is to get the right answer collectively, not to be right.
 </li>
 <li>
 Embrace technological change: We adopt tools and techniques that make us faster, smarter, and better&mdash;especially around AI and automation&mdash;and drop outdated methods without hesitation.
 
 
 </li>
 </ul>
 
 
 Additional Details
 
 
 
 
 <ul>
 <li>
 Location: Fully Remote (all I-9 eligible candidates will be considered).
 </li>
 <li>
 Employment: Full-time. Seniority: Senior professional.
 </li>
 <li>
 Salary: The expected base salary range for this role is $165,000 to $185,000. Actual compensation will be determined based on the candidate's primary work location and other job-related factors including skills, experience, qualifications, interview performance, internal equity, and market data. Candidates located in higher cost-of-living markets, including the San Francisco Bay Area, may be considered within the higher end of the range. This range reflects base salary only and does not include bonus, equity, or benefits; total compensation may also include a discretionary performance-based bonus.
 </li>
 <li>
 Benefits: Medical, dental, and vision; 401(k); and responsible vacation time (PTO).
 </li>
 <li>
 Travel required for team and department offsites. An in-person interview may be required during the process. A broadband internet connection is required. Compliance with Allocate's Code of Ethics is a given for this role.
 
 
 </li>
 </ul>
 Compensation Range: $165K - $185K
 
 
</div>

Capital One

<div>Lead Machine Learning Engineer
As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale. You&rsquo;ll lead the detailed technical design, development, and implementation of core agentic architectures and multi-agent workflows using emerging technologies. You&rsquo;ll focus on system-level architectural design, develop and review complex models and application code, and ensure the high availability, performance, and security of our generative AI applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in generative and agentic machine learning engineering.
What you&rsquo;ll do in the role:
<ul><li>Architect Agentic Platforms: Design, develop, and scale core agentic engines and multi-agent workflow solutions, enabling seamless composition of conversational and business automation workflows.
</li><li>Drive AI Evaluation &amp; Trust: Build and integrate scalable evaluation (Evals) and observability frameworks into solutions to ensure model predictability, performance monitoring, and mitigation of model risk.
</li><li>Deliver High-Impact Use Cases: Partner with cross-functional product and business teams to deploy production AI solutions, including next-generation consumer AI experiences, intelligent recommendation engines, and advanced conversational assistants.
</li><li>Enforce Enterprise Guardrails: Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability.
</li><li>Translate Practical Research: Stay abreast of practical advancements in LLM optimization, retrieval-augmented generation (RAG), and multi-agent design patterns, judiciously applying these novel techniques to production systems.
</li><li>Technical Leadership &amp; Code Excellence: Provide technical direction, architectural oversight, and rigorous code reviews for engineering teams, fostering a culture of modern engineering excellence.
</li></ul>Basic Qualifications:
<ul><li>Bachelor&rsquo;s Degree
</li><li>At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
</li><li>At least 4 years of experience programming with Python, Scala, or Java
</li></ul>Preferred Qualifications:
<ul><li>Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
</li><li>3+ years of experience with GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and Vector Databases
</li><li>3 years of experience building, scaling, and optimizing Large Language Model (LLM) or GenAI orchestration systems in production
</li><li>2+ years of experience building automated evaluations (Evals) and observability pipelines for LLMs
</li><li>3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
</li><li>Experience deploying AI solutions within a strictly regulated environment, incorporating data privacy and model risk governance
</li><li>Demonstrated ability to lead technical architecture design and provide deep technical guidance to engineering teams
</li><li>Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
</li><li>ML industry impact through conference presentations, papers, blog posts, open-source contributions, or patents
</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.
Plano, TX: $179,400 - $204,700 for Lead 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>

Lead Machine Learning Engineer

Atlassian

<div>Overview:

As a Principal Machine Learning Engineer, you will drive the development and implementation of cutting-edge machine learning algorithms, training sophisticated models, and collaborating with product, engineering, and analytics teams to build AI functionalities into Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks &mdash; designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to emerging ML engineers. Your role is pivotal, ensuring AI's transformative potential is realized across our offerings.
Responsibilities:

Technical Leadership &amp; Execution<ul><li>
Drive complex decisions that impact the work of teams and change their technical direction over multiple quarters</li><li>
Regularly tackle the largest and most complex problems on the team, from technical design to launch</li><li>
Set the direction of systems and capabilities, balancing progress over perfection</li><li>
Determine plans-of-attack on large projects and solve complex architecture challenges</li></ul>
Model Development &amp; Experimentation<ul><li>
Design, develop, and deploy production-grade ML models (e.g., ranking, retrieval, LLM-based systems) to optimize user experience and achieve business objectives</li><li>
Conduct meticulous experimentation and model evaluations, backing decisions with data</li><li>
Develop robust feature engineering practices to ingest, process, and serve features for offline training and online inference at scale</li><li>
Oversee end-to-end deployment of ML solutions into production, ensuring continuous evaluation, monitoring, and improvement</li></ul>
Cross-Functional Collaboration<ul><li>
Collaborate closely with product managers, designers, and engineering teams to integrate AI/ML capabilities into products</li><li>
Partner across engineering teams to take on company-wide programs spanning multiple projects</li><li>
Communicate complex technical concepts clearly to both technical and non-technical stakeholders</li></ul>
Mentorship &amp; Organizational Impact<ul><li>
Mentor and guide junior and senior engineers, fostering a culture of innovation, collaboration, and continuous learning</li><li>
Actively share knowledge and expertise through mentoring and coaching beyond direct reports</li><li>
Contribute to programs of work that scale across the department</li><li>
Identify, solve, and bridge gaps/problems across teams using experience and expertise</li></ul>
Decision Making &amp; Direction<ul><li>
Quickly collate and analyze key decision parameters, balancing speed, risk, and impact appropriately</li><li>
Limit ambiguity and risk by experimenting and prototyping</li><li>
Understand how contributions of multiple capabilities fit into larger products and platforms</li></ul>
Compensation:

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.
This role may also be eligible for benefits, bonuses, commissions, and equity.
Pay Ranges:

In The United States or Remote, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A: $209,700 - $273,775

Zone B: $188,730 - $246,398

Zone C: $174,051 - $227,233
Qualifications:

Benefits &amp; Perks
Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.
About Atlassian
At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.
We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.
To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.
To learn more about our culture and hiring process, visit go.atlassian.com/crh.
In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.</div>

Principal Machine Learning Engineer

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

Paramount

<div>
 
 #WeAreParamount on a mission to unleash the power of content&hellip; you in?
 
 
 
 We&rsquo;ve got the brands, we&rsquo;ve got the stars, we&rsquo;ve got the power to achieve our mission to entertain the planet &ndash; now all we&rsquo;re missing is&hellip; YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter &ndash; both for our audiences and our employees &ndash; and aim to leave a positive mark on culture.
 
 
 
 Overview
 
 
 
 As a member of the Applied Machine Learning Group, you&rsquo;ll help build a world-class streaming experience within the team. Your mission is to get the user watching. You own the "onboarding" and "re-entry" experience, including high-commitment surfaces like Your Next Watch (YNW), Jump Back In (JBI), and While You Were Away (WYWA).
 
 
 You&rsquo;ll put customers first by using MLFlow for experiment tracking, Qdrant for retrieval, and Post-training RL to ensure that the content we surface at the start of a session is what the user is most likely to commit to. You will be in charge of implementing the components that solve the "Cold Start" problem for new users and provide seamless re-entry for returning ones. You&rsquo;ll work in an IC2 (Mid-level) role to ensure these surfaces, which optimize for Start Rate under uncertainty, identify if a user stays or leaves within the first 30 seconds of app launch.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Independent Delivery: Own the implementation of features for JBI (Jump Back In) and YNW (Your Next Watch).
 </li>
 <li>
 Retrieval Optimization: Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends.
 </li>
 <li>
 Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a high bar for reproducibility.
 </li>
 <li>
 Model Training: Develop and serve models in GCP using TensorFlow/PyTorch, incorporating Post-training RL for reward-based optimization.
 </li>
 <li>
 Collaborative Quality: Participate in design reviews to ensure your components share the same high-reliability standards as the rest of the pod.
 </li>
 <li>
 Solving Cold Start: Build the logic that makes the app feel personalized even for users we know very little about.
 </li>
 <li>
 High-Commitment Accuracy: Optimize for the "Play" button&mdash;the ultimate signal of user commitment.
 
 
 </li>
 </ul>
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 3+ years in MLE.
 </li>
 <li>
 Experience with GCP and MLFlow.
 </li>
 <li>
 Proficiency in TensorFlow/PyTorch.
 </li>
 <li>
 Knowledge with Vector DBs.
 </li>
 <li>
 Proven ability to implement features that solve the "Cold Start" problem for new users.
 </li>
 <li>
 Experience managing, tracking, and deploying experiments to ensure a high bar for reproducibility.
 
 
 </li>
 </ul>
 Bonus Skills
 
 
 <ul>
 <li>
 Background in multi-stage ranking or "Cold Start" problems.
 </li>
 <li>
 Experience with retrieval optimization and re-entry carousels.
 </li>
 <li>
 Knowledge with Post-training RL for reward-based optimization.
 
 
 </li>
 </ul>
 Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment&trade;. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME&reg;.
 
 
 
 Additional Information
 
 
 
 Hiring Salary Range: $124,000.00 - 186,000.00.
 
 
 The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.
 
 
 
 What We Offer:
 
 
 
 <ul>
 <li>
 Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
 </li>
 <li>
 Generous paid time off.
 </li>
 <li>
 An exciting and fulfilling opportunity to be part of one of Paramount&rsquo;s most dynamic teams.
 </li>
 <li>
 Opportunities for both on-site and virtual engagement events.
 </li>
 <li>
 Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
 </li>
 <li>
 Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount
 
 
 </li>
 </ul>
 Paramount is an equal opportunity employer (EOE) including disability/vet.
 
 
 At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status.
 
 
 If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access https://www.paramount.com/careers as a result of your disability. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.
 
 
</div>

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