learning engineer

LATENT

<div><h1 class="jobSectionHeader">Machine Learning Engineer</h1><h2 class="jobSectionHeader">
About Latent Health</h2>
Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family.
For everyone else, care is fragmented and impersonal.
Medical history is scattered across systems that don&rsquo;t communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly&mdash;but not the individual.
We believe this can be fundamentally rebuilt.
At Latent Health, we are building systems that understand both:<ul><li>
the population (clinical knowledge at scale)</li><li> 
and the individual (longitudinal patient history)</li></ul> 
Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning.
Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth.<h2 class="jobSectionHeader">
ML at Latent Health</h2>
The Machine Learning team is responsible for building systems that run in real clinical workflows.
We work on:<ul><li>
Verifiable reinforcement learning at scale</li><li> 
Mid-training and post-training of foundation models</li><li> 
Novel objectives derived from longitudinal patient data</li></ul> 
We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production.
We are a small team and expect engineers to take ownership of critical systems, not components.<h2 class="jobSectionHeader">
The Role</h2>
As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that run in real clinical workflows.
You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way.
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end.
This role involves owning systems that directly impact real patient outcomes.<h2 class="jobSectionHeader">
What You&rsquo;ll Do</h2><ul><li>
Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure</li><li> 
Train and fine-tune large language models (LLMs) for:<ul><li> 
Clinical reasoning</li><li> 
Medical question answering</li><li> 
Evidence-grounded generation</li></ul></li><li> 
Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments</li><li> 
Develop evaluation frameworks to ensure model safety and clinical validity</li><li> 
Integrate ML systems into product workflows and patient-facing applications</li><li> 
Monitor system performance in production and iterate based on real-world usage and feedback</li><li> 
Define what &ldquo;correct&rdquo; means in ambiguous clinical workflows in collaboration with engineers and clinicians</li></ul><h2 class="jobSectionHeader"> 
What We&rsquo;re Looking For</h2><ul><li>
Strong foundation in machine learning and software engineering</li><li> 
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered</li><li> 
Experience driving ambiguous ML problems from 0 1, including problem formulation, model design, and productionization</li><li> 
Hands-on experience with PyTorch or similar frameworks</li><li> 
Ability to operate independently in high-ambiguity environments with minimal guidance</li><li> 
Strong product and engineering judgment &mdash; you know when to use ML, when not to, and how to scope problems accordingly</li><li> 
Comfort working in a fast-moving, early-stage environment</li><li> 
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)</li></ul><h2 class="jobSectionHeader"> 
Nice to Have</h2><ul><li>
Experience deploying LLMs in production environments</li><li> 
Experience building distributed systems or large-scale data pipelines</li><li> 
Experience working with clinical, biomedical, or other regulated datasets</li></ul><h2 class="jobSectionHeader"> 
Why Join Latent Health</h2><ul><li>
Work on high-stakes problems with real impact on patient care</li><li> 
Build systems that define how AI is trusted in clinical decision-making</li><li> 
Significant ownership in a small, high-caliber team</li><li> 
Competitive compensation and meaningful equity</li></ul><h2 class="jobSectionHeader"> 
Location</h2>
We are based in San Francisco and work together in person.
We spend most of the week in the office and prioritize candidates who are excited to work this way.<h2 class="jobSectionHeader">
Compensation</h2><ul><li>
Base salary: $225,000 &ndash; $300,000+</li><li> 
Meaningful equity in an early-stage, Series A company</li></ul><h2 class="jobSectionHeader"> 
Closing</h2>
If you&rsquo;re interested in building systems that bring truly personalized healthcare to millions of patients, we&rsquo;d love to talk.</div>

Machine Learning Engineer

Cloud Bridge Solutions Inc

<div>
 
 
 Overview
 
 
 
 We are seeking a talented Machine Learning Engineer to design, build, and deploy scalable machine learning models and AI-driven solutions. The ideal candidate has strong programming skills, hands-on experience with ML frameworks, and a passion for solving real-world problems using data.
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Design, develop, and deploy machine learning models for production use
 </li>
 <li>
 Build and maintain scalable data pipelines and feature engineering workflows
 </li>
 <li>
 Train, evaluate, and optimize models using large datasets
 </li>
 <li>
 Collaborate with cross-functional teams to integrate ML solutions into applications
 </li>
 <li>
 Monitor model performance and implement improvements over time
 </li>
 <li>
 Ensure code quality, testing, and documentation for ML systems
 </li>
 </ul>
 
 
 Required Skills &amp; Qualifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, or related field
 </li>
 <li>
 5-10 years of experience in machine learning or data science
 </li>
 <li>
 Strong programming skills in
 
 Python
 
 </li>
 <li>
 Experience with ML frameworks such as
 
 TensorFlow
 
 ,
 
 PyTorch
 
 , or
 
 Scikit-learn
 
 </li>
 <li>
 Solid understanding of algorithms, statistics, and data structures
 </li>
 <li>
 Experience with APIs, microservices, or cloud platforms (AWS/Azure/GCP)
 </li>
 </ul>
 
 
</div>

Spotify

<div>
 The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans. We&rsquo;re looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their promotion strategies, whether it&rsquo;s a DIY artist or an industry-facing partner.
 
 
 As an ML Engineer, you will help execute on strategies for understanding the factors that play a role in the performance of promoted tracks across the globe. You&rsquo;ll build data-driven solutions, as well as effective online and offline strategies to efficiently iterate and evaluate model approaches. You&rsquo;ll have access to a growing list of datasets, features and ML infrastructure to continually experiment and improve the model-based approach.
 
 
 What You'll Do
 
 
 <ul>
 <li>
 Contribute to the design, build, evaluation, shipping, and refinement of systems that improve Spotify&rsquo;s promotional performance with hands-on ML development
 </li>
 <li>
 Collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring they are highly efficient, scalable, and consistently meet well-defined success criteria
 </li>
 <li>
 Influence the technical design, architecture, and infrastructure decisions to support new and diverse machine learning architectures.
 </li>
 <li>
 Work with Data and ML Engineers to support transitioning machine learning models from research and development into production
 </li>
 <li>
 Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule to maintain production stability.
 
 
 </li>
 </ul>
 Who You Are
 
 
 <ul>
 <li>
 You have experience implementing ML systems at scale in Java, Scala, Python or similar languages as well as experience with ML frameworks such as TensorFlow, PyTorch, etc.
 </li>
 <li>
 You have an understanding of how to bring machine learning models from research to production and are comfortable working with innovative, cutting-edge architectures.
 </li>
 <li>
 You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
 </li>
 <li>
 You have experience in optimizing machine learning models for production use cases
 </li>
 <li>
 You preferably have experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP
 </li>
 <li>
 You have some exposure to causal ML models, including things like counterfactuals.
 </li>
 <li>
 You are familiar with creating model success metric dashboards, diagnosing production issues, and are willing to take part in an on-call schedule to maintain performance.
 
 
 </li>
 </ul>
 Where You'll Be
 
 
 We offer you the flexibility to work where you work best! For this role, you can be within the EST time zone as long as we have a work location.
 
 
 The United States base range for this position is $170,000 - $212,000 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
 
 
 Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what&rsquo;s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It&rsquo;s in our differences that we will find the power to keep revolutionizing the way the world listens.
 
 
 At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we&rsquo;re here to support you in any way we can.
 
 
 Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world&rsquo;s most popular audio streaming subscription service.
</div>

Apple

Apple&rsquo;s Health Sensing team is seeking a versatile Machine Learning Engineer to develop next-generation health algorithms that deliver meaningful insights to users by combining classical ML, signal processing, and emerging generative AI techniques. Our team has delivered impactful features including heart rate notifications, ECG, blood oxygen, sleep apnea notifications, and overnight vitals to millions of Apple Watch users. 
 
Description 
 
This role is ideal for an engineer who enjoys moving quickly from idea to prototype to product, creatively overcoming data limitations, and applying new tools to multi-modal sensor fusion problems in health and wellness. You will work across the full algorithm lifecycle including data strategy, modeling, evaluation, optimization, and deployment.&quot;,&quot;responsibilities&quot;:&quot;Develop and validate ML and GenAI-driven algorithms for health sensing applications from concept through productization 
 
Prototype and compare multiple approaches using real and synthetic data to accelerate algorithm development 
 
Design experiments and evaluation methodologies to quantify performance and guide algorithm improvements 
 
Optimize algorithms for robustness, efficiency, and on-device deployment constraints 
 
Work cross-functionally with user studies, hardware, software, and product teams to bring algorithms into product 
 
Analyze failure modes, quantify tradeoffs, and drive data-driven algorithm improvements 
 
Preferred Qualifications 
 
Experience developing algorithms for physiological sensing using multi-modal data 
 
Familiarity with on-device ML frameworks or resource-constrained optimization 
 
Experience working with incomplete, noisy, or limited datasets 
 
Background in experimental design and statistical validation 
 
Experience with distributed or cloud-based ML workflows 
 
Experience accelerating development through simulation, synthetic data, or creative data augmentation approaches 
 
Self-driven, curious engineer comfortable taking ambiguous sensing problems from concept to working solutions 
 
Minimum Qualifications 
 
Bachelors degree in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience. 
 
Strong foundation in machine learning, statistics, signal processing, or applied mathematics for real-world sensing problems 
 
Experience applying modern AI techniques, including generative AI and agentic AI, to accelerate algorithm development, data generation, and performance evaluation 
 
Proficiency in Python for algorithm development and optimization 
 
Demonstrated ability to rapidly prototype, evaluate multiple approaches, and iterate based on experimental results 
 
Experience owning algorithm development from early exploration through validation and integration 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

MORSE Corp

<div>
 
 MORSE Corp is an employee owned, small business based in Cambridge, MA, Arlington, VA, and Seattle, WA with a history of fielding
 
 cutting-edge
 
 
 technology. MORSE boasts a specially selected team of scientists, engineers, and software developers to deliver best-in-class technical solutions that solve difficult multidisciplinary problems faced by the US National Security Ecosystem.
 
 
 &nbsp;
 
 
 
 
 We are seeking a Machine Learning Engineer to join our team at MORSE.  You will play a pivotal role in designing, implementing, and managing complex ML algorithms and systems, with a focus on computer vision (CV) and other types of data. You will be responsible for acquiring truth data, integrating algorithms, testing algorithms, combining algorithms, reviewing literature to stay on top of the latest-and-greatest methods, analyzing data from field tests, and developing advanced algorithms.  MORSE&rsquo;s AI &amp; ML work crosses modalities, and experience or interest in the fields of Large Language Models (LLM), audio analysis, computer vision, and advanced reasoning is a plus. You will work with MORSE&rsquo;s current team of engineers to transition algorithms to production, which may run on on-prem servers, on the cloud, or on a real-time embedded system. You will be part of our team working to accelerate our US National Security customers abilities to use natural language processing capabilities in mission-critical environments.
 
 
 &nbsp;
 
 
 
 Responsibilities:
 
 
 &nbsp;
 
 <ul>
 <li>
 
 Develop, fine-tune, train, and optimize Computer Vision algorithms processing tasks such as object detection and tracking.&nbsp;
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Use MLOps tools for efficient experiment tracking, data management, and reproducibility
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Write robust, efficient, and maintainable code
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Track the latest advancements with machine learning research to bring new techniques and methodologies to MORSE
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Conduct experiments and perform rigorous evaluations to assess the effectiveness and efficiency of CV models
 
 
 &nbsp;
 
 
 </li>
 </ul>
 
 Skills and Requirements:
 
 
 &nbsp;
 
 <ul>
 <li>
 
 US CITIZENSHIP REQUIRED and the ability to obtain a U.S. Security Clearance
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Masters or Ph.D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or related field
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Proven experience in applying CV models, techniques, frameworks, and libraries to implement and fine-tune models
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Proven experience testing and validating the performance of AI technologies in real-world applications
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Proficiency in Python
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Experience with cloud platforms (AWS and Azure)
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Experience with Docker
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Experience with MLOps tools such as Airflow, MLFlow, AimStack, etc.
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Exceptional communication skills and the ability to work well with customers
 
 
 &nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Understanding of Department of Defense requirements and standards is a plus
 
 
 &nbsp;
 
 </li>
 </ul>
 
 
 &nbsp;
 
 
 
 
 
 
 &nbsp;
 
 
 
 
 &nbsp;
 
 
 
 MORSE Corp&rsquo;s salary range for this role carefully considers a wide range of compensation factors, including but not limited to, prior experience, education, skills and expertise, location, internal equity, and other factors that are job related and consistent with business need. Therefore, final offer amounts may vary from the amount stated. Depending on role eligibility, total compensation may also include bonus, stock, 401(k) match, paid time off, medical, dental, vision and life insurance.Employees also receive 10 paid holidays per year. MORSE maintains an &ldquo;open&rdquo; leave policy that does not restrict exempt, regular full-time employees to a specific&nbsp;number of paid sick or vacation days. However, this policy is not an &ldquo;unlimited&rdquo; paid leave policy.&nbsp;
 
 
 Compensation: $90,000 USD - $210,000 USD
 
 
 The team at MORSE takes pride in being the smart team that is easy to work with. We focus on steady, long-term success with an emphasis on exceptionalism and delivering mission-critical impact to our customers. For more information, please visit www.morsecorp.com.
 
 
 
 &nbsp;
 
 
</div>

Tyndale Company, Inc.

<div>
 The Tyndale Company is seeking a
 
 Machine Learning Engineer
 
 to join our growing AI organization. This role will play a critical part in our mission to harness data for advanced AI and Machine Learning applications. You will collaborate with data scientists, data engineers and business stakeholders to own the full lifecycle of our ML models and Generative AI applications. This is an exciting opportunity to shape the foundation of our AI and Machine Learning efforts, driving impactful insights and innovations.
 
 
 
 HYBRID/REMOTE:
 
 Tyndale supports a strong work-life balance. This opportunity requires onsite work a minimum of 1 day per week, and 4 days per week remotely.
 
 To be considered, candidates must reside within a commutable distance from our corporate headquarters in Pipersville, PA (Bucks County) or our location in Houston, TX (City Centre).
 
 
 
 The Tyndale Company is a private, 9x Top Workplace winner in PA and 5x winner in TX, and an industry leading national supplier of arc-rated flame-resistant clothing (FRC) to the energy sector &ndash; including utilities, oil and gas, transportation, chemical manufacturing, and NFPA 70E markets. We&rsquo;re a family-owned business providing a retail-style apparel experience to hundreds of thousands of energy workers across the US and Canada. We&rsquo;re the leading distributor of innovative FRC solutions, and the largest industrial supplier of Carhartt FR, Ariat FR, and Wrangler FR clothing.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Own the Full Lifecyle: Own the full lifecycle of classical ML and GenAI features from prototypes to production. This includes rapid POCs, automated tests, containerisation, and production deployment through our AWS pipeline.
 </li>
 <li>
 RAG &amp; LLM work: Build retrieval-augmented generation services using vector stores (OpenSearch, Pinecone, PGVector, etc.), prompt-orchestration frameworks (LangChain / LlamaIndex), and fine-tuning or supervised-RL when off-the-shelf LLMs fall short.
 </li>
 <li>
 Create and maintain inference APIs: Design, implement, and document REST / GraphQL / SSE endpoints (FastAPI or AWS Lambda) so other teams and external partners can consume models with zero guess-work.
 </li>
 <li>
 Infrastructure-as-Code: Write and maintain Terraform / AWS CDK modules that one-click deploy training jobs, inference services, monitoring, and alerting.
 </li>
 <li>
 Feature engineering &amp; inference services: Design reusable feature pipelines in PySpark / Pandas on top of the lakehouse; expose real-time inference via FastAPI or AWS Lambda.
 </li>
 <li>
 Evaluation &amp; guardrails: Define offline and online evaluation matrices, set up hallucination/bias checks, and wire metrics into Grafana/DataDog.
 </li>
 <li>
 Cross-functional collaboration: Pair with the Senior Data Scientist on modelling strategy, the Data Engineer on schema evolution, and our CloudOps team on infrastructure tweaks&mdash;then demo real impact to business stakeholders.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
 </li>
 <li>
 Minimum of 4 years in ML Engineering, with at least 1 year building LLM or generative AI products in production.
 </li>
 <li>
 Pro-level Python; hands-on with PyTorch or TensorFlow/Keras; comfortable writing optimized SQL code.
 </li>
 <li>
 Familiar with LangChain/LlamaIndex, vector databases, and prompt-engineering strategies.
 </li>
 <li>
 Production experience on AWS (SageMaker, Bedrock, Lambda, Step Functions, ECS/EKS).
 </li>
 <li>
 Infrastructure-as-Code &ndash; hands-on with Terraform to codify everything: training pipelines, SageMaker endpoints, Lambda/Fargate deploys, monitoring alerts.
 </li>
 <li>
 API design &amp; implementation &ndash; proven experience exposing ML models through FastAPI, Flask, or equivalent REST/GraphQL/SSE frameworks, including auth, versioning, and OpenAPI docs.
 </li>
 <li>
 Solid software-engineering habits&mdash;CI/CD (GitHub Actions), code reviews, unit/integration testing.
 </li>
 <li>
 Ability to translate model metrics into plain-English business value.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Health &amp; Wellness : Comprehensive medical, dental, and vision insurance with competitive premiums. Paid parental leave. Mental health support through an EAP and partial reimbursement on copays, fertility support, and robust wellness programs with annual reimbursements.
 </li>
 <li>
 Work-Life Balance : Many positions with Tyndale offer hybrid onsite + remote work schedules, generous PTO, paid holidays + a floating holiday, and more.
 </li>
 <li>
 Financial Compensation : Competitive salary, 401(k) with matching, and bonus opportunities.
 </li>
 <li>
 Career Growth &amp; Development : Training/certification/tuition reimbursement programs and demonstrated paths for knowledge share and internal promotion opportunity.
 </li>
 <li>
 Culture &amp; Perks : Family-owned values, award winning culture, team-engagement events, casual dress code, company-sponsored charitable events and activities, and an inclusive workplace that values collaboration and integrity.
 </li>
 </ul>
</div>

Meltwater

<div>
 
 Description
 
 
 
 Meltwater, a pioneer of media intelligence and now Outside Insight, gives businesses the information advantage they need to stay ahead. More than 30,000 companies have used Meltwater&rsquo;s media intelligence to stay on top of billions of online conversations and extract relevant insights to strategically manage their brands. Meltwater is dedicated to personal, global service built on the local expertise of 55 offices across six continents. Meltwater is also committed to fostering the data science ecosystem through MEST, a pan-African entrepreneurial program and incubator, and Shack15, a global data science community.
 
 
 Our AI team is young, ambitious and focused on innovation.
 
 
 With Meltwater, we are more than 2000 people across the globe, so there is a lot going on. We believe in diversity and inclusion, and building teams with a wide variety of backgrounds and perspectives. This approach not only raises the quality of our products for our customers, but makes collaboration along the way much more fun.
 
 
 Read our underthehood blog to see which problems our engineers are solving.
 
 
 Meltwater&rsquo;s Consumer Intelligence AI Team is looking for a Machine Learning Engineer to help improve the multimodal semantic layer of MIRA, our AI assistant, while also developing innovative new features that power the next generation of applied generative AI in the Consumer Intelligence industry. This includes building agentic capabilities that allow MIRA to reason across data sources, orchestrate multi-agent/multi-step workflows, and generate complex, use-case driven reports for customers.
 
 
 We are looking for someone with a creative, proactive, collaborative, and innovation focused mindset. You will work alongside a team of passionate machine learning engineers driven by enthusiasm for adopting new technologies and excitement about making the difference for customers using applied AI.
 
 
 As a Machine Learning Engineer at Meltwater, you will own machine learning powered features from idea to production, working on systems that operate at massive scale. You should be comfortable with experimentation, collaboration, and real-world impact.
 
 
 
 What we're looking for:
 
 
 
 This role is ideal for candidates with a strong foundation in machine learning, data science, or applied research who enjoy experimentation and model-driven problem solving and who want to translate their work into real-world, production systems at scale. You are excited to grow your software engineering and production skills over time, while bringing strong foundations in experimentation, modeling, evaluation, and applied machine learning.
 
 
 Our team is organized over 3 objectives : innovation, research and production.
 
 
 
 Innovation:
 
 We leverage our AI expertise to constantly imagine new business oriented features to enhance our products.
 
 
 
 Research:
 
 To implement our algorithms, we need to stay up-to-date to the latest breakthroughs over the Machine Learning world as well as the latest frameworks.
 
 
 
 Production
 
 : We make a point of designing and developing our algorithms with production-grade best practices. Scalability is our goal. The models and systems that we develop are running over billions of documents (text or image) per day.
 
 
 
 Responsibilities:
 
 
 
 <ul>
 <li>
 Drive the development of industrial ML projects from ideation to deployment, contributing across early prototyping, research, evaluation, and progressively to production, scalability, and monitoring with support from the team.
 </li>
 <li>
 Collaborate closely with product teams in their domains, building domain expertise, and leveraging ML to solve critical business problems
 </li>
 <li>
 Keep up-to-date with the latest advancements in machine learning to stay current on emerging techniques to solve novel problems
 </li>
 <li>
 Design, run, and iterate on machine learning experiments to improve model quality and product impact
 </li>
 <li>
 Develop features, online and offline evaluation metrics, and validation strategies for large-scale ML systems
 </li>
 <li>
 Analyze model errors and failure modes to understand limitations, biases, and opportunities for improvement.
 
 
 </li>
 </ul>
 
 What You'll Do:
 
 
 
 <ul>
 <li>
 Drive the development of new industrial level projects from ideation to deployment. This includes deep expertise in areas such as early ideation &amp; prototyping, research, infrastructure, scalability, monitoring, and evaluation of AI systems.
 </li>
 <li>
 Collaborate closely with product teams in their domains, building domain expertise, and leveraging ML to solve critical business problems
 </li>
 <li>
 Keep up-to-date with the latest advancements in machine learning to stay current on emerging techniques to solve novel problems
 </li>
 <li>
 You&rsquo;ll write efficient, well-tested and design code, build clean systems to empower MIRA with new advanced generative AI capabilities
 </li>
 <li>
 Demonstrable experience with the full lifecycle of machine learning models - from development to deployment and monitoring
 </li>
 <li>
 Experience building industry-scale user-facing ML/AI/Agentic applications
 </li>
 <li>
 Experience with MLOps and DevOps principles and technologies (e.g. CI/CD, Terraform, Containerization, Kubernetes)
 </li>
 <li>
 Experience with Information Retrieval systems in a production environment (Elasticsearch, Quickwit, Vespa, &hellip;) is a plus
 
 
 </li>
 </ul>
 
 What You'll Bring:
 
 
 
 <ul>
 <li>
 2+ years experience applying machine learning techniques through academic, professional, or personal projects, ideally with exposure to experimentation, modeling, or applied research.
 </li>
 <li>
 Solid foundation in machine learning concepts such as feature engineering, model selection, training, validation, and error analysis
 </li>
 <li>
 Ability to write clear, well-structured code, with motivation to grow software engineering skills and contribute to production ML systems
 </li>
 <li>
 Experience reasoning about metrics, tradeoffs, experiments, and uncertainty in applied ML problems
 </li>
 <li>
 You are comfortable learning new tools, concepts, or techniques as needed, and enjoy expanding beyond your initial area of strength.
 </li>
 <li>
 Advanced knowledge in a least one programming language (Python or other e.g. Rust, Scala, Java)
 </li>
 <li>
 Passion for AI (in particular Generative AI), with a constant drive to stay updated and ahead of the curve
 </li>
 <li>
 You are a team-player, self-learner and autonomous
 </li>
 <li>
 MS, PhD, or equivalent practical experience in machine learning, data science, or a related technical field
 
 
 </li>
 </ul>
 
 What We Offer:
 
 
 
 <ul>
 <li>
 You will work with a team of passionate people who are always willing to help and learn from others
 </li>
 <li>
 You will have the opportunity to apply the most recent literature to concrete AI projects with a real impact on business
 </li>
 <li>
 As a growing company, we combine a startup mindset with the resources necessary to commit to exciting new initiatives. At Meltwater, everyone has an opportunity to make a difference
 </li>
 <li>
 Enjoy flexible paid time off that allows you to have an enhanced work-life balance.
 </li>
 <li>
 Excellent medical, dental, and vision options
 </li>
 <li>
 401(k) matching, life insurance, commuter benefits, and parental leave plans
 </li>
 <li>
 Complimentary CalmApp subscription for you and your loved ones, because mental wellness matters.
 </li>
 <li>
 Energetic work environment with a hybrid work style, providing the balance you need.
 </li>
 <li>
 Thrive within our inclusive community and seize ongoing professional development opportunities to elevate your career.
 </li>
 <li>
 Total compensation range for this position: $150-200 per year (Depending on insurance)
 
 
 </li>
 </ul>
 
 Where You'll Work:
 
 
 
 New York
 
 
 
 When you'll join:
 
 
 
 May/June 2026
 
 
 
 Our Story
 
 
 
 At Meltwater, we believe that when you have the right people in the right environment, great things happen.
 
 
 Our best-in-class technology empowers our
 
 27,000 customers
 
 around the world to make better business decisions through data. But we can&rsquo;t do that without our global team of developers, innovators, problem-solvers, and high-performers who embrace challenges and find new solutions for our customers.
 
 
 Our award-winning global culture drives everything we do and creates an environment where our employees can make an impact, learn every day, feel a sense of belonging, and celebrate each other&rsquo;s successes along the way.
 
 
 We are innovators at the core who see the potential in people, ideas and technologies. Together, we challenge ourselves to go big, be bold, and build best-in-class solutions for our customers.
 
 
 We&rsquo;re proud of our diverse team of
 
 2,200+ employees
 
 in
 
 50 locations
 
 across
 
 25 countries
 
 around the world. No matter where you are, you&rsquo;ll work with people who care about your success and get the support you need to unlock new heights in your career.
 
 
 
 We are Meltwater. Inspired by innovation, powered by people.
 
 
 
 
 Our Team
 
 
 
 Our AI team is young, ambitious and focused on innovation. With Meltwater, we are more than 2000 people across the globe, so there is a lot going on. We believe in diversity and inclusion, and building teams with a wide variety of backgrounds and perspectives. This approach not only raises the quality of our products for our customers, but makes collaboration along the way much more fun.
 
 
 Read our under-the-hood blog to see which problems our engineers are solving.
 
 
 
 Equal Employment Opportunity Statement
 
 
 
 
 Meltwater is an Equal Opportunity Employer and Prohibits Discrimination and Harassment of Any Kind: At Meltwater, we are dedicated to fostering an inclusive and diverse workplace where every employee feels valued, respected, and empowered. We are committed to the principle of equal employment opportunity and strive to provide a work environment that is free from discrimination and harassment.
 
 
 
 
 All employment decisions at Meltwater are made based on business needs, job requirements, and individual qualifications, without regard to race, color, religion or belief, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, marital status, veteran status, or any other status protected by the applicable laws and regulations.
 
 
 
 
 Meltwater does not tolerate discrimination or harassment of any kind, and we actively promote a culture of respect, fairness, and inclusivity. We encourage applicants of all backgrounds, experiences, and abilities to apply and join us in our mission to drive innovation and make a positive impact in the world.
 
 
 We may use AI to assist in aspects of our hiring process, such as reviewing candidate information and supporting evaluation activities. These tools are used only to assist our team through increased efficiencies. Every output continues to be reviewed by humans, and all hiring decisions are made by humans. We remain committed to evaluating candidates fairly, consistently, and in compliance with all applicable laws
 
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