software engineer

data engineer

ai engineer

ai engineer ii

engineer

python engineer

Open Systems Technologies

<div>
 A major financial firm is looking for a
 
 Python Developer
 
 to join their team in New York, NY.
 
 
 
 Compensation: $150-200k/year
 
 
 
 
 Qualifications
 
 
 
 <ul>
 <li>
 A Bachelor's Degree in Computer Science, Engineering, or related field of study
 </li>
 <li>
 5+ years of relevant hands-on technical experience
 </li>
 <li>
 Strong Python skills
 </li>
 <li>
 Strong understanding of relational databases and SQL
 </li>
 <li>
 Strong analytical skills with attention to detail
 </li>
 <li>
 Strong communication skills
 </li>
 </ul>
</div>

Python Developer

Intellect Group

<div>
 
 
 AI Engineer &ndash; New York City
 
 
 
 
 Location:
 
 New York, NY (Hybrid)
 
 
 
 Salary:
 
 $110,000 &ndash; $140,000 depending on experience
 
 
 
 Type:
 
 Permanent | Full-Time
 
 
 
 
 
 Intellect Group is actively placing AI Engineers with high-growth technology and AI-first companies across New York City.
 
 
 
 
 
 Whether you're a recent grad with strong fundamentals or an early-career engineer who's already shipped AI into production, we want to hear from you. We partner with funded AI startups, fintech innovators, and established technology businesses who are hiring AI engineering talent right now &mdash; and New York's AI scene has never moved faster.
 
 
 
 
 
 This is your chance to work on real problems, with real stakes, at companies that are building the next generation of intelligent systems.
 
 
 
 
 
 
 What You'll Be Doing
 
 
 
 
 
 
 &bull; Designing, building, and deploying machine learning models and AI-powered features into production
 
 
 &bull; Working with LLMs, NLP, and deep learning frameworks to solve meaningful business problems
 
 
 &bull; Building and maintaining end-to-end AI pipelines in collaboration with data scientists and engineering teams
 
 
 &bull; Experimenting with prompt engineering, fine-tuning, and retrieval-augmented generation (RAG)
 
 
 &bull; Contributing to model evaluation, testing, monitoring, and continuous improvement
 
 
 &bull; Staying close to the latest AI research and translating it into practical, scalable applications
 
 
 
 
 
 
 What We're Looking For
 
 
 
 
 
 
 &bull; A degree in Computer Science, AI, Mathematics, Data Science, or a related field (Master's a plus, not a must)
 
 
 &bull; Strong Python skills and hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face
 
 
 &bull; Solid understanding of machine learning fundamentals &mdash; neural networks, transformers, classification, regression, and model evaluation
 
 
 &bull; Exposure to LLMs, generative AI, or NLP through coursework, projects, internships, or personal builds
 
 
 &bull; A problem-solving mindset, intellectual curiosity, and a genuine drive to build things that work
 
 
 &bull; Clear communication skills &mdash; you can explain what you built, why it matters, and what you'd do differently
 
 
 
 
 
 
 Bonus Points
 
 
 
 
 
 
 &bull; Experience with cloud platforms: AWS, GCP, or Azure
 
 
 &bull; Familiarity with vector databases, embeddings, or RAG pipelines
 
 
 &bull; MLOps experience &mdash; Docker, FastAPI, CI/CD, or similar deployment tooling
 
 
 &bull; Open-source contributions, Kaggle competitions, or side projects that show you build for fun
 
 
 &bull; Prior experience in fintech, media, healthcare, or enterprise SaaS (all strong sectors in the NYC AI market)
 
 
 
 
 
 
 What's on Offer
 
 
 
 
 
 
 &bull; $110,000 &ndash; $140,000 base salary depending on experience
 
 
 &bull; Roles across Manhattan, Brooklyn, and hybrid-friendly companies throughout the metro area
 
 
 &bull; Fast-moving environments where your work ships and has real impact
 
 
 &bull; Clear growth path into senior and staff AI engineering roles
 
 
 &bull; The support of a specialized recruitment team with deep roots in the AI, data, and machine learning market
 
 
 
 
 
 
 Apply Now
 
 
 
 
 
 
 If you have strong technical foundations and a genuine interest in building AI systems that matter, apply today and a member of the Intellect Group team will be in touch.
 
</div>

Artificial Intelligence Engineer

Edra

<div>
 
 About Edra
 
 
 
 Edra is solving one of the hardest problems in enterprise AI: AI models are generic but company processes are specific. We build AI agents that learn how processes actually run, and then run their operations.
 
 
 We're a Series A startup, backed by Sequoia and other leading VC firms, and we're growing our team in New York and London. We're a deeply technical team of engineers, AI researchers, and strategists with a high bar for talent and a shared belief that exceptional people are the foundation of everything great we'll build.
 
 
 
 The Role
 
 
 
 We're looking for Backend Engineers who care deeply about the craft of building software: strong typing, thoughtful API design, robust data modeling, and systems that are a pleasure for other engineers to work with. You won't be building AI models, you'll be building everything around them: the platform, workflows, interfaces, integrations, and observability that make our AI useful in the real world. As an early engineer at Edra, you'll shape not just the product but the engineering culture, tooling choices, and technical foundations the company is built on.
 
 
 We work deliberately and deeply. There's no separation between "building it" and "shipping it"--you'll go from whiteboard to production and own the outcome. We'd rather build something thoughtfully than ship something fragile, and we treat velocity and quality as complementary, not competing.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 Design and build platform systems in Python (Fast API), from API contracts to database schema and infrastructure
 </li>
 <li>
 Build and maintain connectors, integrations, and data pipelines that connect our platform to external systems
 </li>
 <li>
 Build internal libraries, tooling, and interfaces that make the rest of the engineering team more productive
 </li>
 <li>
 Own your work fully: gather context, manage dependencies, and drive tasks to completion without a product manager handing you tickets
 </li>
 <li>
 Wear multiple hats across the stack&ndash;you might design a schema in the morning, build the API layer in the afternoon, and debug a deployment issue before end of day
 </li>
 <li>
 Participate in weekly product meetings, understand how customers and forward deployed engineers use what you build, and shape what gets built next
 </li>
 <li>
 Collaborate daily with a small, high-caliber team of engineers and AI specialists
 </li>
 <li>
 Have direct influence on technical direction: choose tools, set patterns, and establish conventions the team will build on for years
 
 
 </li>
 </ul>
 
 What We&rsquo;re Looking For
 
 
 
 <ul>
 <li>
 3+ years of experience in software engineering (but a computer science degree is not necessarily required)
 </li>
 <li>
 A love of writing Python and/or TypeScript to a genuinely high standard&ndash;well-modeled, well-typed, and well-documented. Strong typing feels like a superpower, not a chore
 </li>
 <li>
 Experience building libraries, SDKs, or internal tooling that made other developers' lives easier (and you&rsquo;ve enjoyed it)
 </li>
 <li>
 You light up when designing a clean database schema and take real pride in getting data models right
 </li>
 <li>
 You're at your best when you own a problem end to end &mdash; comfortable with ambiguity and energized by turning a vague problem into a well-scoped solution
 </li>
 <li>
 You're drawn to building foundational systems from scratch &mdash; greenfield work where your decisions compound over time excites you
 
 
 </li>
 </ul>
 Don't meet all of the above? We'd still love to hear from you. We're looking for exceptional people with unique skills and interests, and we know that great people have different backgrounds and skillsets. If your primary interest is building agentic systems, working with LLMs, or doing applied ML, check out our AI Engineering role&ndash;it might be a better fit.
 
 
 
 Learn More About Edra
 
 
 
 <ul>
 <li>
 Watch our Series A announcement video
 </li>
 <li>
 Read our blog post from our launch
 </li>
 <li>
 Learn more about what we're building
 
 
 </li>
 </ul>
 
 A Few Details
 
 
 
 Edra is an equal opportunity employer and we encourage applications from individuals of all backgrounds. Edra does not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, marital status, sexual orientation, gender identity, veteran status, or any other legally protected characteristic.
 
 
 Compensation Range: $195K - $275K
 
 
</div>

Software Engineer - Back End (New York)

Fusemachines

<div>
 
 About Fusemachines
 
 
 
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 
 
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 
 
 
 Type: Full-time, Remote
 
 
 
 
 Role Overview
 
 
 
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 
 
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Problem Framing &amp; Stakeholder Partnership
 </li>
 <ul>
 <li>
 Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
 </li>
 <li>
 Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
 </li>
 </ul>
 <li>
 Data Analysis &amp; Feature Engineering
 </li>
 <ul>
 <li>
 Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
 </li>
 <li>
 Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
 </li>
 <li>
 Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
 </li>
 </ul>
 <li>
 Model Development (Core ML)
 </li>
 <ul>
 <li>
 Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
 </li>
 <li>
 Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
 </li>
 <li>
 Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
 </li>
 <li>
 Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
 </li>
 <li>
 Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
 </li>
 </ul>
 <li>
 Deep Learning
 </li>
 <ul>
 <li>
 Build and train deep learning models using PyTorch or TensorFlow/Keras
 </li>
 <li>
 Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
 </li>
 </ul>
 <li>
 Evaluation, Explainability, and Iteration
 </li>
 <ul>
 <li>
 Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
 </li>
 <li>
 Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
 </li>
 </ul>
 <li>
 Productionization &amp; MLOps (Project-Dependent)
 </li>
 <ul>
 <li>
 Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
 </li>
 <li>
 Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
 </li>
 </ul>
 <li>
 Documentation &amp; Communication
 </li>
 <ul>
 <li>
 Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
 </li>
 <li>
 Create documentation and lightweight demos that make results actionable
 
 </li>
 </ul>
 </ul>
 Success in This Role Looks Like
 
 
 <ul>
 <li>
 You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
 </li>
 <li>
 Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
 </li>
 <li>
 Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
 </li>
 <li>
 Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
 </li>
 <li>
 Strong SQL skills (joins, window functions, aggregation, performance awareness)
 </li>
 <li>
 Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
 </li>
 <li>
 Hands-on experience across multiple model types, including:
 </li>
 <ul>
 <li>
 Classification &amp; regression
 </li>
 <li>
 Time series forecasting
 </li>
 <li>
 Clustering/segmentation
 </li>
 </ul>
 <li>
 Experience with deep learning in PyTorch or TensorFlow/Keras
 </li>
 <li>
 Strong problem-solving skills: ability to work with ambiguous goals and messy data
 </li>
 <li>
 Clear communication skills and ability to translate analysis into decisions
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
 </li>
 <li>
 Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
 </li>
 <li>
 Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
 </li>
 <li>
 Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
 </li>
 <li>
 Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
 </li>
 <li>
 Consulting or client-facing delivery experience
 
 
 </li>
 </ul>
 
 Certifications (Strong Plus)
 
 
 
 Candidates with at least one relevant certification are especially encouraged to apply:
 
 
 <ul>
 <li>
 Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
 </li>
 <li>
 Databricks certifications (Data Scientist, Data Engineer, or related)
 
 
 </li>
 </ul>
 
 Nice-to-Have
 
 
 
 <ul>
 <li>
 Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
 </li>
 <li>
 Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
 </li>
 <li>
 Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
 
 
 </li>
 </ul>
 
 
 Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
 
 
 
 
 Important: Immigration Sponsorship Policy
 
 
 Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
 
 
 <ul>
 <li>
 Direct Company Sponsorship: Such as H-1B, J-1, or TN visas
 </li>
 <li>
 Employer of Record: Listing Fusemachines as the immigration employer on any government documentation
 </li>
 <li>
 Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT)
 
 
 </li>
 </ul>
 Powered by JazzHR
 
 
 qUN1H2divA
</div>

Machine Learning Engineer / Data Scientist

Netbuilder

Location: New York (office-based) with travel to client sites as requiredSalary: Up to $140,000 per year (dependent on experience)Role type: Full-time, permanentDepartment: Technology Consulting &ndash; Observability PracticeNETbuilder is building an Observability Practice, and this is a senior delivery hire into it. You will join as a senior data pipeline engineer, working hands-on inside enterprise client teams to architect, deploy and operate the observability platforms and telemetry pipelines that sit at the heart of modern observability.This is primarily a hands-on delivery role &ndash; a senior individual-contributor position rather than a line-management one. Your focus is engineering across two fronts: designing and deploying the observability platforms themselves, and building the data pipelines that collect, route, transform, enrich and deliver telemetry reliably and cost-effectively across complex enterprise environments. You will be the senior technical authority on observability platform architecture and data pipelines for the engagements you work on. While there is no permanent team to manage, on specific engagements you will provide technical leadership &ndash; mentoring junior engineers, planning the workstream and allocating tasks across the delivery team.Consulting experience is preferred but not essential &ndash; what matters most is deep, current, hands-on expertise with industry-standard observability platforms and their pipeline and transformation tooling. If you would rather solve hard data-engineering and platform problems and lead through expertise than carry permanent people-management responsibility, this is the role.Key Responsibilities:Pipeline Engineering &amp; Delivery<ul><li>Design, build and operate telemetry pipelines that collect, route, transform, enrich, reduce and mask logs, metrics and traces at enterprise scale</li><li>Develop and maintain data transformation logic &ndash; parsing, restructuring, filtering and normalising telemetry across formats and schemas</li><li>Optimise pipelines for data quality, cost control and performance &ndash; reducing noise and volume without losing signal</li><li>Onboard complex applications and data sources onto observability platforms</li><li>Build, test and document reusable pipeline configurations, processors and templates</li><li>Monitor pipeline health and throughput, and troubleshoot ingestion, routing and delivery issues</li></ul>Platform Architecture &amp; Deployment<ul><li>Architect observability platform deployments &ndash; topology, sizing, high availability, resilience and scaling &ndash; to meet enterprise client requirements</li><li>Deploy, configure and operate observability platforms and their collection/agent fleets across cloud, hybrid and on-premises environments</li><li>Define deployment patterns and infrastructure-as-code for repeatable, automated platform rollouts</li><li>Integrate observability platforms with client infrastructure, data sources and downstream destinations</li><li>Own platform health, upgrades and capacity planning, and keep architecture and deployment documentation current</li></ul>Client Delivery<ul><li>Deliver observability platform and pipeline engagements directly with enterprise clients as a senior practitioner</li><li>Own delivery outcomes for the platform and pipeline workstreams on assigned engagements</li><li>Act as senior technical authority and escalation point for platform architecture, deployment and data pipeline issues</li><li>Lead troubleshooting and root-cause analysis across enterprise observability platforms and telemetry pipelines</li><li>Support pre-sales with technical scoping and solution design for platform and pipeline work</li><li>Translate client requirements into platform and pipeline designs that balance coverage, cost and compliance</li></ul>Engagement Leadership &amp; MentoringThis is engagement-scoped technical leadership rather than permanent line management &ndash; on specific engagements you will:<ul><li>Provide technical leadership as the senior practitioner, setting direction and quality standards for the platform and pipeline workstream</li><li>Plan the workstream &ndash; scope the work, sequence the tasks and allocate them across the engagement delivery team where required</li><li>Mentor and coach junior engineers and consultants working alongside you, sharing platform and pipeline expertise on the job</li><li>Co-ordinate with the wider engagement team and report progress on the workstream</li></ul>Tooling &amp; Practice Contribution<ul><li>Work hands-on across the observability tool landscape (see preferred tools below), applying each platform's architecture, deployment and data-pipelining capabilities</li><li>Stay current with platform capabilities, roadmaps and certifications</li><li>Contribute to NETbuilder's platform and pipeline methodology, toolkits and reusable assets</li></ul>Qualifications and Experience:<ul><li>Senior data pipeline engineering experience: demonstrable hands-on track record designing, building and operating telemetry/observability data pipelines at enterprise scale</li><li>Platform architecture and deployment experience: hands-on experience designing, deploying and operating observability platforms (topology, sizing, high availability and scaling) across cloud, hybrid and on-premises environments</li><li>Data transformation expertise: strong practical experience with parsing, routing, filtering, enrichment and normalisation of log, metric and trace data</li><li>Tooling experience: hands-on experience with the architecture, deployment and data-pipelining functionality of one or more industry-standard observability platforms (see preferred tools below)</li><li>Engagement leadership: comfortable mentoring junior engineers and planning and allocating tasks within a delivery team on an engagement, without formal line-management authority</li><li>Consulting experience: preferred but not essential &ndash; comfortable working directly within enterprise client teams</li></ul>Preferred Tool Experience:Hands-on experience with the architecture, deployment and data-pipelining functionality of one or more of the following platforms, in order of preference:<ul><li>Bindplane (ObservIQ / Google)</li><li>Dynatrace (OpenPipeline)</li><li>OpenTelemetry (Collector &ndash; receivers, processors and exporters)</li><li>Cribl (Stream)</li><li>Elastic (ingestpipelines)</li><li>Splunk</li></ul>Preferred Certifications:Certification in one or more of the following is preferred:<ul><li>Dynatrace Professional (Dynatrace Master advantageous)</li><li>OpenTelemetry Certified Associate (OTCA) &ndash; also covers Bindplane, which is OpenTelemetry-native</li><li>Cribl Certified Engineer (CC Engineer) or Cribl Certified Observability Engineer &ndash; Admin (CCOE Admin)</li><li>Elastic Certified Engineer</li><li>Splunk Enterprise Certified Architect</li></ul>Key Skills:<ul><li>Platform architecture and deployment: designing observability platform topology (sizing, HA, scaling) and deploying/automating platform and pipeline infrastructure as code</li><li>Pipeline and transformation tooling: deep expertise in one or more observability platforms and their transformation languages and processors</li><li>OpenTelemetry: strong working knowledge of the OpenTelemetry Collector (receivers, processors, exporters) and OTLP</li><li>Scripting and automation: Python, Bash</li><li>Data formats and parsing: JSON, regex, syslog, and common log, metric and trace formats</li><li>Cloud platform knowledge: AWS, Azure, and/or GCP</li><li>Container and orchestration: Kubernetes, Docker, OpenShift</li><li>Familiar with delivery and project tooling: JIRA, Confluence, ServiceNow</li></ul>About You:<ul><li>A hands-on engineer who would rather architect and build a solution than sit in a management meeting</li><li>Happy to lead through expertise &ndash; mentoring others, planning a workstream and giving direction on an engagement, even without a formal management title</li><li>Comfortable working directly inside enterprise client teams and engaging senior stakeholders</li><li>Rigorous about data quality, cost and reliability &ndash; you care about getting the right signal at the right price</li><li>Self-managing, delivery-focused and comfortable in a fast-moving environment</li><li>Naturally curious &ndash; you keep pace with a fast-evolving observability tooling landscape</li></ul>About NETbuilder:NETbuilder is a leading provider of digital solutions, software, consulting and managed services. We work across multiple sectors with specialist expertise in financial services, government, and commercial markets. Since 1999 we have been providing end-to-end solutions across Digital Delivery, Development and Technology. At our core we are a Digital Transformation consultancy with capabilities for onsite, onshore and offshore delivery.You will join a world-class team of experienced consultants, helping to build a new observability practice within the NETbuilder group.Why NETbuilder:<ul><li>Join a new observability practice as a senior technical specialist, working with cutting-edge observability platforms</li><li>Work with enterprise clients across multiple sectors</li><li>Competitive base salary: $120,000 - $140,000 (dependent on experience)</li><li>Continuous investment in professional development and certification</li><li>High-performance, close-knit team culture where individual contribution is recognised</li><li>Office-based New York, with travel to enterprise client sites</li></ul>Pay: Up to $140,000.00 per yearBenefits:<ul><li>Health insurance</li><li>Paid time off</li></ul>Application Question(s):<ul><li>Do you have a demonstrable hands-on track record of designing, building and operating telemetry/observability data pipelines?</li></ul>Ability to Commute:<ul><li>New York, NY 10036 (Required)</li></ul>Willingness to travel:<ul><li>25% (Required)</li></ul>Work Location: In person

Senior Data Pipeline Engineer

Capital One

<div>Senior Manager, Software Engineering, Full Stack
Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs. We are seeking Full Stack Software Engineers who are passionate about marrying data with emerging technologies. As a Capital One Sr. Manager Software Engineer, you&rsquo;ll have the opportunity to be on the forefront of driving a major transformation within Capital One. 
What You&rsquo;ll Do:<ul><li>
Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company</li><li>
Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal &amp; external technology communities, mentoring other members of the engineering community, and from time to time, be asked to code or evaluate code</li><li>
Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment</li><li>
Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services</li></ul> 
Basic Qualifications:<ul><li>
Bachelor&rsquo;s Degree</li><li>
At least 6 years of experience in software engineering (Internship experience does not apply)</li><li>
At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)</li><li>
At least 4 years of people management experience</li></ul> 
Preferred Qualifications:<ul><li>
Master&rsquo;s Degree</li><li>
9+ years of experience in at least one of the following: JavaScript, Java, TypeScript, SQL, Python, or Go</li><li>
3+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service</li><li>
4+ years of experience in open source frameworks</li><li>
7+ years of people management experience</li><li>
2+ years of experience in Agile practices</li><li>
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion</li></ul> 
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
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.
New York, NY: $250,800 - $286,200 for Sr. Mgr, Software Engineering 
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>

Senior Manager, Software Engineering, Full Stack

Colgate-Palmolive

<div></div><div><div><div>Requisition ID 174332 - Posted 06/18/2026 - Analytics - United States - New York - New York - Colgate-Palmolive - Travel - up to 10% of time - On-site</div></div><div><div><div><div> 
</div></div></div></div> 
<div></div><div><div><div><div><div><div><div>No Relocation Assistance Offered 
Job Number #174332 - New York, New York, United States 
 
Who We Are 
 
Colgate-Palmolive Company is a global consumer products company operating in over 200 countries specializing in Oral Care, Personal Care, Home Care, Skin Care, and Pet Nutrition. Our products are trusted in more households than any other brand in the world, making us a household name! 
 
Join Colgate-Palmolive, a caring, innovative growth company reimagining a healthier future for people, their pets, and our planet. Guided by our core values&mdash;Caring, Inclusive, and Courageous&mdash;we foster a culture that inspires our people to achieve common goals. Together, let's build a brighter, healthier future for all.</div> 
<div>*This role can sit in our Park Ave (NYC) or Piscataway, NJ office*</div> 
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Role Summary</div>

We are seeking a Machine Learning Engineer who brings the analytical rigor of a data scientist and the engineering discipline of a software architect. In support of Colgate-Palmolive&rsquo;s purpose to Make More Smiles and our commitment to a healthier future for our people, pets, and planet, this role builds the advanced machine learning capabilities that power smarter decisions, accelerate innovation, and create measurable impact across our global enterprise.

As part of the Enterprise AI/ML Center of Excellence, you will lead the architectural design and end-to-end execution of high-priority ML initiatives. This involves integrating statistical modeling, optimization, and autonomous workflows into Colgate-Palmolive's business processes to accelerate innovation, enhance decision intelligence, and embed AI. Beyond hands-on technical work, you ensure solutions are architecturally sound, production-ready, and compliant with enterprise governance standards, translating strategy into robust execution aligned with stakeholder needs and long-term value creation. 
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Responsibilities:</div>
<ul>
<li><div>Productionize ML Research: Transition experimental models into robust, scalable production services. You don't just build the model; you build the pipeline that sustains it.</div>
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<li><div>Pipeline Orchestration: Design and maintain complex data and ML pipelines using Airflow and dbt to ensure data integrity and model reliability.</div>
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<li><div>Statistical Rigor: advanced statistical modeling and hypothesis testing to validate models, ensuring outcomes are testable and honest.</div>
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<li><div>DevOps &amp; MLOps: Utilize modern developer tools to work within and CI/CD frameworks for ML and software lifecycle management</div></li></ul> 
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</div><div>Required Qualifications:</div>
<ul>
<li><div>Bachelor&rsquo;s Degree (or higher) in a high-rigor field: Statistics, Physics, Chemistry, Mathematics, Data Science, or Computer Science with a heavy emphasis on Statistical Learning.</div>
</li>
<li><div>Experience: Bachelors degree: 6+ of years of technical experience; Masters or PhD (3+ years)</div></li></ul> 
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</div><div>Preferred Qualifications:</div>
<ul>
<li><div>Proven expertise in Data Science and/or Machine Learning Engineering.</div>
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<li><div>Advanced proficiency in Python (Production-grade) and SQL.</div>
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<li><div>Hands-on experience with Airflow for orchestration and dbt for transformation.</div>
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<li><div>Familiarity with modern IDEs and Agentic Coding systems (e.g., Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.</div>
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</ul><ul><li><div>Modern Stack: Expert knowledge of Python, Scikit-learn, major ML Libraries</div>
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<li><div>Data Engineering: Deep understanding of data lifecycle (ETL/ELT), data architecture, best practices for templatized data transformation</div>
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<li><div>Engineering Excellence: Familiar with Docker/Kubernetes, CI/CD, Git, and &quot;Software Engineering for ML&quot; best practices.</div>
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<li><div>LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle</div></li> 
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Compensation and Benefits 
Salary Range $130,000.00 - $170,000.00 USD 
 
Pay is determined based on experience, qualifications, and location. Salaried employees may also be eligible for discretionary bonuses, profit-sharing, and long-term incentives for Executive-level roles. 
 
Benefits: Salaried employees enjoy a comprehensive benefits package, including medical, dental, vision, basic life insurance, paid parental leave, disability coverage, and participation in the 401(k) retirement plan with company matching contributions subject to eligibility requirements. Additional benefits include a minimum of 15 vacation/PTO days (hourly employees receive a minimum of 120 hours) and 13 paid holidays (vacation days are prorated based on the employee's hire date within the calendar year). Paid sick leave is adjusted based on role and location in accordance with local laws. Detailed information regarding paid sick leave entitlements will be provided to employees upon hiring and may be subject to adjustments based on changes in legislation or company policies. 
 
Our Commitment to Inclusion 
Our journey begins with our people&mdash;developing strong talent with diverse backgrounds and perspectives to best serve our consumers around the world and fostering an inclusive environment where everyone feels a true sense of belonging. We are dedicated to ensuring that each individual can be their authentic self, is treated with respect, and is empowered by leadership to contribute meaningfully to our business. 
 
Equal Opportunity Employer 
Colgate is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, ethnicity, age, disability, marital status, veteran status (United States positions), or any other characteristic protected by law. 
 
Reasonable accommodation during the application process is available for persons with disabilities. Please complete this request form should you require accommodation. 
 
For additional Colgate terms and conditions, please click here. 
 
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Machine Learning Engineer (174332)

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