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ml engineer

Catalyst Labs

<div>
 
 About The Job ML Engineer
 
 
 
 
 Our Client
 
 
 
 Is a rapidly growing Tier 1 VC backed startup based in New York with $60 million in funding revolutionizing how outside sales and service teams work. Their AI technology captures and analyzes real-world conversations, providing full visibility into every customer interaction without the need for traditional ride-alongs.
 
 
 By turning field conversations into searchable, actionable data, they empower teams to coach more effectively, close more deals, and boost average ticket sizes. Combining cutting-edge AI with a deep understanding of field sales dynamics, this company is redefining how businesses learn from and optimize their in-person customer experiences.
 
 
 
 About Us
 
 
 
 Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We stand out as an agency that&rsquo;s deeply embedded in our clients&rsquo; recruitment operations.
 
 
 We collaborate directly with Founders, CTOs, and Heads of AI in those themes who are driving the next wave of applied intelligence from model optimization to productized AI workflows. We take pride in facilitating conversations that align with your technical expertise, creative problem-solving mindset, and long-term growth trajectory in the evolving world of intelligent systems.
 
 
 
 Location
 
 : New York, NY
 
 
 
 Work type
 
 : Full Time,
 
 
 
 Compensation
 
 : above market base + bonus + equity
 
 
 
 Roles &amp; Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecyclefrom conception to deployment and maintenance.
 </li>
 <li>
 Architect and deliver AI-powered solutions enabling natural speech interaction and real-time audio understanding.
 </li>
 <li>
 Develop and optimize ML models focused on audio data to extract business-critical insights from previously unstructured voice data.
 </li>
 <li>
 Build agents capable of operating natively on real-world audio inputs.
 </li>
 <li>
 Collaborate with cross-functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications.
 </li>
 <li>
 Work directly with customers to identify needs, gather feedback, and deliver impactful real-world solutions.
 </li>
 <li>
 Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments.
 </li>
 <li>
 Participate in continuous improvement of the ML infrastructure and processes for scalability and performance.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
 </li>
 <li>
 1-6 years of professional experience in ML engineering.
 </li>
 <li>
 Strong programming skills in Python (TypeScript experience is a plus).
 </li>
 <li>
 Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
 </li>
 <li>
 Familiarity with cloud environments and infrastructure (preferably AWS).
 </li>
 <li>
 Strong understanding of data pipeline design, real-time inference, and model monitoring.
 </li>
 <li>
 Excellent communication skills with the ability to engage directly with customers and stakeholders.
 
 
 </li>
 </ul>
 
 Core Experience
 
 
 
 <ul>
 <li>
 Proven experience building and deploying ML models into production environments.
 </li>
 <li>
 Demonstrated ability to own the full model lifecyclefrom data ingestion and model development to deployment and monitoring.
 </li>
 <li>
 Experience with audio-focused ML projects or similar domains involving unstructured data.
 </li>
 <li>
 Proficiency in building scalable data pipelines for model training and evaluation.
 </li>
 <li>
 Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 is a plus.
 </li>
 <li>
 Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.
 </li>
 </ul>
</div>

ML Engineer

JPMorganChase

<div>JOB DESCRIPTION 
This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.
As a AI/ML Engineering Associate at JPMorgan Chase within the Business Banking you will be a key member responsible for designing and delivering trusted, market-leading AI technology products. Your role will involve implementing critical technology solutions across various technical domains to support the firm&rsquo;s strategic objectives, ensuring security, stability, and scalability.
Job Responsibilities:<ul><li>
Define the AI‑driven validation roadmap across Business Banking.</li><li>
Architect and embed end‑to‑end AI checks into campaign authoring and execution workflows for all channels.</li><li>
Build AI/ML models to enhance final reads (pre‑launch approvals), incorporating external signals and trend analysis to generate actionable recommendations.</li><li>
Partner with Marketing, , Legal, Compliance, and Controls to operationalize AI capabilities and standardize measurement across CCB sub‑LOBs.</li><li>
Automate quality metrics and evolve the learning agenda using internal benchmarks and external best practices.</li><li>
Provide executive‑ready insights, recommendations, and status updates on the portfolio roadmap and key learnings.</li><li>
Integrate with upstream and downstream systems to ensure seamless tooling and support Transformation objectives.</li><li>
Champion quality‑as‑a‑service: prevent errors and drive continuous improvement in validation processes.</li><li>
Demonstrate customer obsession by understanding and improving the end‑to‑end customer experience.</li></ul>
Required qualifications, capabilities and skills:<ul><li>
Formal training or certification in Software Engineering concepts and 1+ years applied experience</li><li>
Experience of Gen AI builds, Agents, and MCP.</li><li>
Experience in one of the programming languages like Python, typescript, streamlit, UI design. Intermediate Python is a must.</li><li>
Strong background in Natural Language Processing (NLP) and Large Language Models (LLMs)</li><li>
Hands-on experience with machine learning and deep learning methods.</li><li>
Proficiency in AI and machine learning frameworks (e.g., TensorFlow, PyTorch).</li><li>
Strong programming skills in languages such as Python, R, or Java.</li><li>
Ability to design and implement complex algorithms for recommendation systems and trend analysis.</li><li>
Experience in data integration and real-time data processing.</li><li>
Understanding of marketing and campaign testing processes.</li></ul>
Preferred Qualifications:<ul><li>
Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.</li><li>
Experience of all LLM models and their capabilities</li><li>
Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.</li><li>
Prior experience in credit card marketing and in-market testing.</li><li>
Excellent communication and presentation skills along with strong interpersonal skills and ability to translate technical concepts into actionable business strategies.</li><li>
Demonstrated initiative and creativity with a proven track record in leading AI tool development and implementation.</li></ul>
ABOUT US 
Chase is a leading financial services firm, helping nearly half of America&rsquo;s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans 
 
 
ABOUT THE TEAM 
 
Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We&rsquo;re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction.</div>

AI/ML Software Engineer II

James Search Group

<div>
 
 
 AI/ML Engineer
 
 
 
 
 Location:
 
 New York, NY
 
 
 
 Compensation:
 
 $135,000 &ndash; $175,000 base + bonus + full benefits, (401(k), and more)
 
 
 
 Industry:
 
 Global P&amp;C Insurance
 
 
 
 
 
 
 About the role and the company.
 
 
 
 James Search Group is working closely with a top-tier global property &amp; casualty insurance carrier to identify an
 
 AI/ML Engineer
 
 to join their enterprise Data and Artificial Intelligence division. This team is driving innovation across the business by developing and scaling AI capabilities that unlock new value and improve decision-making across underwriting, claims, and customer engagement.
 
 
 
 
 
 As an AI/ML Engineer, you'll collaborate with senior team members to build, refine, and deploy predictive models and intelligent systems. The ideal candidate is skilled in analytics, curious by nature, and passionate about transforming complex data into practical solutions that deliver measurable business impact.
 
 
 
 
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Support the development and deployment of AI/ML models&mdash;including classification, forecasting, uplift and propensity modeling, and foundational model tuning.
 </li>
 <li>
 Collaborate with cross-functional teams to identify use cases where data science can enhance operational efficiency and business performance.
 </li>
 <li>
 Partner with business, IT, and strategy teams to ensure AI models are scalable, impactful, and aligned with organizational goals.
 </li>
 <li>
 Communicate analytical findings clearly to stakeholders with varying levels of technical knowledge.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications:
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative discipline; Ph.D. a plus.
 </li>
 <li>
 Minimum of 2 years&rsquo; experience applying advanced analytics methods (e.g., decision trees, logistic regression, neural networks, random forests).
 </li>
 <li>
 Proficiency in R or Python for analytics and model development, including work with both structured and unstructured data.
 </li>
 <li>
 Proven ability to manage and integrate large, complex datasets and an understanding of end-to-end data modeling processes.
 </li>
 <li>
 Strong written and verbal communication skills with the ability to connect technical outputs to business strategy.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications:
 
 
 <ul>
 <li>
 Internship or project experience in machine learning, AI engineering, or related roles&mdash;especially in insurance or financial services.
 </li>
 <li>
 Familiarity with actuarial concepts or insurance-specific data models.
 </li>
 <li>
 Enthusiastic about continued learning in AI/ML and staying current with new tools and methodologies.
 </li>
 <li>
 Comfortable in a collaborative, fast-paced, and agile working environment.
 </li>
 </ul>
 
 
 
 
 This is a unique opportunity to grow your career with a highly respected organization investing in next-generation analytics.
 
 
 
 
 
 
 Apply today
 
 to help reshape the future of insurance through data and AI innovation.
 
</div>

Machine Learning Engineer

Impact.Com

<div>About impact.com 
impact.com is the world's leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L'Or&eacute;al, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale &mdash; sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization &mdash; making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer &mdash; and to be genuinely excited about both.

What You'll Do:

Yield Optimization &amp; Pricing Models
<ul>
<li>Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.</li>
<li>Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.</li>
<li>Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.</li>
<li>Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.</li>
</ul>
Inventory Allocation &amp; Supply Optimization
<ul>
<li>Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.</li>
<li>Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.</li>
<li>Identify and address inefficiencies in inventory utilization &mdash; including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.</li>
</ul>
Data Architecture &amp; Feature Engineering
<ul>
<li>Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.</li>
<li>Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance &mdash; at the scale of programmatic data volumes.</li>
<li>Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.</li>
</ul>
Real-Time Inference &amp; Production ML
<ul>
<li>Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.</li>
<li>Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.</li>
<li>Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.</li>
<li>Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.</li>
</ul>
Experimentation &amp; Performance Measurement
<ul>
<li>Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.</li>
<li>Build evaluation frameworks that go beyond offline metrics &mdash; validating model behavior in live auction environments where feedback signals are delayed or noisy.</li>
<li>Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.</li>
</ul>
Self-Learning Systems &amp; Feedback Loops
<ul>
<li>Research and implement adaptive, self-learning components within the programmatic stack &mdash; including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.</li>
<li>Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.</li>
<li>Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.</li>
</ul>
Cross-Functional Collaboration
<ul>
<li>Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.</li>
<li>Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.</li>
<li>Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.</li>
</ul>
What You Bring:

Required
<ul>
<li>Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).</li>
<li>Programmatic &amp; marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.</li>
<li>Production ML engineering: Proven ability to take models from prototype to production independently &mdash; including real-time inference, monitoring, retraining pipelines, and SLO ownership.</li>
<li>Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.</li>
<li>Technical skills:</li>
<li><ul><li>Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).</li>
<li>Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).</li>
<li>Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.</li>
<li>Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.</li>
</ul></li><li>Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).</li>
<li>Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.</li>
<li>Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.</li>
</ul>
Preferred / Nice to Have
<ul>
<li>Direct experience with SSP, DSP, or exchange-side yield optimization &mdash; particularly floor price optimization, bid landscape modeling, or deal matching algorithms.</li>
<li>Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.</li>
<li>Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.</li>
<li>Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.</li>
<li>Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).</li>
<li>Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.</li>
<li>Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.</li>
<li>Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.</li>
</ul>
What Sets You Apart
<ul>
<li>Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist &mdash; you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.</li>
<li>Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.</li>
<li>Feedback loop thinking. You don't just deploy models &mdash; you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.</li>
<li>Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.</li>
<li>Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.</li>
<li>Collaborative depth. You build genuine technical trust with engineering and product partners &mdash; not just by having good ideas, but by following through, communicating clearly, and making the integration easy.</li>
</ul>
Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.
<ul><li>This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.</li></ul>

Benefits and Perks:

At impact.com, we believe that when you're happy and fulfilled, you do your best work. That's why we've built a benefits package that supports your well-being, growth, and work-life balance.
<ul>
<li>Medical, Dental, and Vision insurance</li>
<li>Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled</li>
<li>Flexible spending accounts and 401(k)</li>
<li>Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.</li>
<li>Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.</li>
<li>A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.</li>
<li>Investing in Your Growth: We're committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.</li>
<li>Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.</li>
<li>Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses.</li>
</ul>
impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors. 


#LI_NewYork_NewYork</div>

Sr. Data Scientist, Programmatic Algorithms

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 - 5 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</div>

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
We are building something transformational - a greenfield, enterprise-grade data and insights platform that will redefine how automobile dealers, OEMs, and third-party partners engage with Amazon Autos. 
 
This is a foundational tech lead opportunity to architect and own the systems that power data-driven decision-making for thousands of dealers and partners across multiple marketplaces. Our seller and partner ecosystem - spanning dealer groups, OEMs, and 3P integrations - depends on real-time, actionable intelligence to optimize inventory, pricing, engagement, and sales performance. You will be the technical cornerstone of the platform that delivers this intelligence at scale. 
 
We are looking for a talented senior engineer with a tech lead mindset: someone who raises the bar on system design, drives architectural decisions across teams, and builds platforms that outlast any single feature. You will work at the intersection of data engineering, AI/ML, and product - owning the full lifecycle from architecture to production. 
 
What You Will Build 
<ul><li>AI-powered data platforms that serve ML models and surface predictive insights to dealers and partners</li><li>Intelligent agents that democratize data access, enabling non-technical stakeholders to query and act on complex datasets</li></ul>
- Next-generation pipelines that leverage AI agents to write, optimize, and validate code - accelerating development velocity while maintaining engineering rigor 
<ul><li>This platform will process massive volumes of transactional, behavioral, and market data, transforming raw signals into insights that directly influence revenue streams.</li></ul> 
What Makes This Role Unique 
This is a rare greenfield opportunity to be the founding technical architect of a platform that will serve as the data backbone for Amazon Autos' seller and partner ecosystem. You won't just leverage AI tools - you'll build the next generation of AI-enabled data platforms, working at the forefront of AI-powered data engineering within a team that has already driven AI adoption to over 90% among its software builders. 
 
Key job responsibilities 
- Platform Architecture: Architect and own the end-to-end design of a full-stack data platform - from data ingestion and transformation pipelines to API delivery and front-end consumption layers. 
<ul><li>Technical Standards: Define and enforce technical standards across the team, including design patterns, data modeling best practices, API contracts, reliability, and scalability principles.</li><li>Cross-Functional Leadership: Lead cross-functional technical initiatives, aligning engineering, product, data science, and partner teams around a unified data strategy.</li><li>AI-Powered Engineering: Build AI-powered data platforms that serve ML models and deploy intelligent agents that democratize data access for non-technical stakeholders.</li><li>Data Pipeline Design: Design and implement large-scale ETL/ELT pipelines using AWS-native and big data technologies, ensuring data freshness, accuracy, and lineage.</li><li>Partner API Strategy: Drive the API strategy for external partner integrations &mdash; enabling OEMs, dealer groups, and 3P systems to consume standardized, real-time data feeds.</li><li>Engineering Excellence: Lead design reviews, define code quality standards, own CI/CD pipelines, and champion automated testing and observability.</li></ul>
- Mentorship: Mentor and grow engineers on the team - conducting design reviews, providing technical guidance, and elevating the overall engineering bar. 
<ul><li>Business Translation: Partner with product and business stakeholders to translate ambiguous business problems into well-scoped, high-impact technical solutions.</li><li>Operational Excellence: Define SLAs, build monitoring and alerting, and lead incident response for production systems.</li></ul> 
A day in the life 
You start your morning reviewing a design proposal from a junior engineer - you provide structured feedback that sharpens the data model and catches a scalability risk before it reaches production. By midday, you're in a cross-team sync with the OEM integrations team, aligning on the API contract for a new real-time inventory feed. In the afternoon, you're deep in architecture work - designing the next generation of the dealer analytics pipeline that will reduce data latency from hours to minutes. You close the day reviewing a pull request and unblocking a teammate on a tricky ETL edge case. Every day, your decisions shape the platform that hundreds of dealers and partners rely on to run their business on Amazon. 
 
About the team 
We are transforming Amazon Autos' data infrastructure into a global, enterprise-grade data service. Our initiative builds a scalable API layer serving OEMs, dealer groups, and internal teams through standardized automated reports and real-time integrations. 
 
This role involves working with massive datasets and running analysis to understand customer behavior for Amazon Autos, collaborating with product managers, designers, and engineers to create automotive shopping experiences that delight customers. Our vision is to create automotive discovery and shopping experiences on Amazon that provide customers with greater convenience and selection.</div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>5+ years of non-internship professional software development experience</li><li>5+ years of programming with at least one software programming language experience</li><li>5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience as a mentor, tech lead or leading an engineering team</li><li>Experience with data modeling, warehousing and building ETL pipelines</li><li>Experience analyzing large datasets and translating them to actionable business insight</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Master's degree in computer science or equivalent</li><li>5+ years of developing end-to-end analytics solutions, including system configuration, model building, data security, and reporting experience</li><li>Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.</li><li>Experience in Redshift, or experience in Hive/Spark/Hbase/Yarn and experience in any Bigdata architecture</li><li>Experience in Kafka, or experience in any Bigdata architecture and experience in Redshift</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, NY, New York - 184,900.00 - 250,200.00 USD annually 
USA, VA, Arlington - 168,100.00 - 227,400.00 USD annually</div>

Sr. Software Development Engineer, Amazon Autos

Techfellow Limited

<div>
 
 
 [Up to c. $450k Comp Package | Office-Led Working]
 
 
 
 
 
 
 
 Role Overview
 
 
 
 
 
 
 We&rsquo;re partnering with a top-tier, technology-led trading firm where machine learning plays a central role in decision-making and execution. This hire will sit within a highly specialised ML group, contributing to the design and evolution of the firm&rsquo;s research and production ML ecosystem. This is a hands-on engineering role operating close to research. You&rsquo;ll help build the systems, tooling, and infrastructure that enable rapid experimentation and efficient deployment of models - while also contributing to how machine learning is applied across a fast-moving, data-rich environment...
 
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 <ul>
 <li>
 Design and develop infrastructure supporting model training, evaluation, and production deployment
 </li>
 <li>
 Improve research workflows to accelerate iteration speed and feedback loops for ML experimentation
 </li>
 <li>
 Build clean, scalable tooling and APIs that make ML systems intuitive and efficient to use
 </li>
 <li>
 Collaborate with researchers to translate theoretical models into robust, production-ready solutions
 </li>
 <li>
 Evaluate and apply a broad range of modelling techniques depending on the problem space
 </li>
 <li>
 Maintain high-quality, reproducible research codebases with strong usability and structure
 </li>
 <li>
 Contribute to architectural decisions shaping the firm&rsquo;s ML platform and tooling ecosystem
 </li>
 <li>
 Stay close to emerging ML techniques, frameworks, and hardware developments, integrating where valuable
 </li>
 </ul>
 
 
 
 
 
 What You&rsquo;ll Bring&hellip;
 
 
 
 
 
 <ul>
 <li>
 3-8 years&rsquo; experience in machine learning engineering, applied ML, or similar technical roles
 </li>
 <li>
 Strong academic or practical grounding in mathematics (e.g. optimisation, linear algebra, probability)
 </li>
 <li>
 Experience building ML systems end-to-end, from experimentation through to production deployment
 </li>
 <li>
 Hands-on experience with modern ML frameworks (e.g. PyTorch, JAX, TensorFlow or similar)
 </li>
 <li>
 Proven ability to structure and maintain clean, reliable, and reproducible research code
 </li>
 <li>
 Solid software engineering fundamentals, including system design and API development
 </li>
 <li>
 Genuine interest in state-of-the-art ML research and willingness to explore new techniques
 </li>
 <li>
 Ability to critically assess modelling approaches and choose appropriate methods for different problems
 </li>
 <li>
 Strong communication skills and ability to collaborate closely with researchers and engineers
 </li>
 </ul>
 
 
 
 
 ...
 
</div>

Machine Learning Platform Engineer (Research-Focused) | Technology-Driven Quantitative Trading Firm

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