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Mercor

<div>
 <strong>
  About The Job
  <br/>
  <br/>
 </strong>
 <strong>
  Mercor
 </strong>
 connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include
 <strong>
  Benchmark
 </strong>
 ,
 <strong>
  General Catalyst
 </strong>
 ,
 <strong>
  Peter Thiel
 </strong>
 ,
 <strong>
  Adam D'Angelo
 </strong>
 ,
 <strong>
  Larry Summers
 </strong>
 , and
 <strong>
  Jack Dorsey
 </strong>
 .
 <br/>
 <br/>
 <strong>
  Position:
 </strong>
 ML Engineer (Coding Agent Experience)
 <br/>
 <br/>
 <strong>
  Type:
 </strong>
 <strong>
  Contract
  <br/>
  <br/>
 </strong>
 <strong>
  Compensation:
 </strong>
 <strong>
  $85/hour
  <br/>
  <br/>
 </strong>
 <strong>
  Location:
 </strong>
 <strong>
  Remote
  <br/>
  <br/>
 </strong>
 <strong>
  Role Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
  </li>
  <li>
   Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
  </li>
  <li>
   Identify bugs, edge cases, performance issues, and failure modes in AI models.
  </li>
  <li>
   Compare outputs from multiple frontier models and assess their strengths and weaknesses.
  </li>
  <li>
   Apply professional engineering judgment to realistic ML engineering scenarios.
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
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  <br/>
 </strong>
 <strong>
  Must-Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   2+ years of professional machine learning engineering experience.
  </li>
  <li>
   Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
  </li>
  <li>
   Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
  </li>
  <li>
   Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred
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  <br/>
 </strong>
 <ul>
  <li>
   Experience deploying ML systems to production.
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation &amp; Legal
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 </strong>
 <ul>
  <li>
   $400 per accepted task
  </li>
  <li>
   Compensation is tied to accepted work.
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Application Process (Takes 20&ndash;30 mins to complete)
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Upload resume
  </li>
  <li>
   AI interview based on your resume
  </li>
  <li>
   Submit form
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Resources &amp; Support
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  </li>
  <li>
   For any help or support, reach out to: support@mercor.com
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
 </em>
</div>


AI/ML Engineer - Fully Remote | Upto $85/hr

Colgate-Palmolive

<div></div><div><div><div>Requisition ID <b>174332</b> - Posted <b>06/18/2026</b> - <b>Analytics</b> - <b>United States</b> - <b>New York</b> - <b>New York</b> - <b>Colgate-Palmolive</b> - <b>Travel - up to 10% of time</b> - <b>On-site</b></div></div><p></p><div><div><div><div><p><b><br>
</b></p></div></div></div></div><br>
<div></div><div><div><div><div><div><div><div>No Relocation Assistance Offered<br>
Job Number #174332 - New York, New York, United States<br>
<br>
<b>Who We Are</b><br>
<br>
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!<br>
<br>
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><br>
<div><b>*This role can sit in our Park Ave (NYC) or Piscataway, NJ office*</b></div><br>
<div></div>
<div><b>
Role Summary</b></div>
<p>
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.</p>
<p>
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.</p><br>
<div></div>
<div><b>
Responsibilities:</b></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>
</li>
<li><div>Pipeline Orchestration: Design and maintain complex data and ML pipelines using Airflow and dbt to ensure data integrity and model reliability.</div>
</li>
<li><div>Statistical Rigor: advanced statistical modeling and hypothesis testing to validate models, ensuring outcomes are testable and honest.</div>
</li>
<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><br>
<div>
</div><div><b>Required Qualifications:</b></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><br>
<div>
</div><div><b>Preferred Qualifications:</b></div>
<ul>
<li><div>Proven expertise in Data Science and/or Machine Learning Engineering.</div>
</li>
<li><div>Advanced proficiency in Python (Production-grade) and SQL.</div>
</li>
<li><div>Hands-on experience with Airflow for orchestration and dbt for transformation.</div>
</li>
<li><div>Familiarity with modern IDEs and Agentic Coding systems (e.g., Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.</div>
</li>
</ul><ul><li><div>Modern Stack: Expert knowledge of Python, Scikit-learn, major ML Libraries</div>
</li>
<li><div>Data Engineering: Deep understanding of data lifecycle (ETL/ELT), data architecture, best practices for templatized data transformation</div>
</li>
<li><div>Engineering Excellence: Familiar with Docker/Kubernetes, CI/CD, Git, and &quot;Software Engineering for ML&quot; best practices.</div>
</li>
<li><div>LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle</div></li><br>
</ul>
<div><br>
<b>Compensation and Benefits</b><br>
Salary Range $130,000.00 - $170,000.00 USD<br>
<br>
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.<br>
<br>
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.<br>
<br>
<b>Our Commitment to Inclusion</b><br>
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.<br>
<br>
<b>Equal Opportunity Employer</b><br>
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.<br>
<br>
Reasonable accommodation during the application process is available for persons with disabilities. Please complete this request form should you require accommodation.<br>
<br>
For additional Colgate terms and conditions, please click here.<br>
<br>
#LI-On-site</div></div></div></div></div></div></div></div><p></p>

Machine Learning Engineer (174332)

Fusemachines

<div>
 <strong>
  About Fusemachines
  <br/>
  <br/>
 </strong>
 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.
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 <strong>
  Type: Full-time, Remote
  <br/>
  <br/>
 </strong>
 <strong>
  Role Overview
  <br/>
  <br/>
 </strong>
 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.
 <br/>
 <br/>
 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.
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 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <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
    <br/>
   </li>
  </ul>
 </ul>
 Success in This Role Looks Like
 <br/>
 <br/>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Certifications (Strong Plus)
  <br/>
  <br/>
 </strong>
 Candidates with at least one relevant certification are especially encouraged to apply:
 <br/>
 <br/>
 <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)
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice-to-Have
  <br/>
  <br/>
 </strong>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  <em>
   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.
   <br/>
   <br/>
  </em>
 </em>
 Important: Immigration Sponsorship Policy
 <br/>
 <br/>
 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:
 <br/>
 <br/>
 <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)
   <br/>
   <br/>
  </li>
 </ul>
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</div>


Machine Learning Engineer / Data Scientist

Databricks

<div><h1 class="jobSectionHeader"><b>Staff Machine Learning Engineer, CustomerLake (ML/LLM)</b></h1>
<p><i>RDQ427R109</i></p><p><br>
</p><p>At Databricks, we are passionate about enabling data teams to solve the world's toughest problems &mdash; from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best Data Intelligence Platform so our customers can use deep data insights to improve their business. Founded by engineers &mdash; and customer obsessed &mdash; we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.</p><p><br>
</p><p>As one of the first engineers in the NYC Engineering office, you'll join a small, nimble team building new products from the ground up. We're building CustomerLake, the Customer Data Platform on Databricks, to bring enterprise-grade ML and AI personalization to every company whose data already lives on Databricks. The best B2C and B2B brands have historically relied on in-house ML/AI teams to power personalization, recommendations, churn and lifetime-value modeling, and audience targeting. Our goal is to deliver that same capability to companies that don't have an in-house team but already have their data in order on Databricks. This is a true 0-to-1 environment, combining the excitement of a startup with the resources of a tech leader like Databricks.</p><p><br>
</p><p><b>The impact you'll have:</b></p>
<ul><li>Evaluate ML and LLM approaches for CustomerLake's personalization use cases, push the models and algorithms forward, and continuously improve quality over time</li>
<li>Go deep on how models behave in production: inspect individual traces, understand how the models reason, and tune and improve from there</li>
<li>Build the platform and evaluation framework that let CustomerLake customers optimize for real business value such as purchases, retention, and product usage, not vanity metrics like email opens and clicks</li>
<li>Push the team toward new directions and novel methods worth tackling, not just optimizing what already exists</li>
<li>Partner closely with product management, engineering, and design to turn ambiguous customer problems into scalable, trustworthy solutions</li>
<li>Set the technical foundation and best practices for our ML/AI personalization work as we grow this into several roles across our products over the next 1-2 years</li></ul><br>
<p></p><p><b>What we look for:</b></p>
<ul><li>10+ years of engineering experience, with a strong foundation across the full loop of shipping and improving ML/AI products</li>
<li>Hands-on experience building and evaluating ML models and/or LLM systems for real product or business use cases; your understanding is practical, not purely academic, and you can make models work well inside a product</li>
<li>Experience with personalization based on customer behavior (ideal) or transactions (acceptable), such as recommendations, targeting, churn, or lifetime-value modeling</li>
<li>Proficiency in Python and modern ML frameworks (e.g., PyTorch), with hands-on experience in model evaluation and monitoring AI quality in production</li>
<li>Familiarity with LLMs and generative AI, including techniques like retrieval-augmented generation (RAG), prompt design, fine-tuning, and evaluation</li>
<li>A demonstrated product mindset, with the ability to translate ambiguous customer problems into scrappy MVPs and iterate quickly based on data and user feedback</li>
<li>High ownership and bias for action in 0-to-1 environments: comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption</li></ul><br>
<p></p><p><b>Nice to have:</b></p>
<ul><li>Experience in martech, ideally a go-to-market or business use case with an analytical (rather than purely transactional) angle</li>
<li>An academic or research background that can help us innovate and develop novel methods</li></ul><br>
<p></p><div><div><div><p><b>Pay Range Transparency</b></p>
<p>Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.<br>
<br>
</p></div><div>Local Pay Range</div>
<div>$192,000&mdash;$260,000 USD
</div></div></div><div><p><b>About Databricks</b></p>
<p>Databricks is the data and AI company. More than 10,000 organizations worldwide &mdash; including Comcast, Cond&eacute; Nast, Grammarly, and over 50% of the Fortune 500 &mdash; rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark&trade;, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
<br>
<br>
<b>Benefits
</b><br>
<br>
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.<br>
<br>
<br>
</p><p><b>Our Commitment to Diversity and Inclusion</b></p>
<p>At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.</p>
<p><b>Compliance</b></p>
<p><b>If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.</b></p></div></div>

Staff Software Engineer - AI/ML

NYU Langone Health

<div><p>NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.
<br>
<br>
<i>For more information, go to</i> <i>med.nyu.edu</i>, and interact with us on <i>LinkedIn</i>, <i>Glassdoor</i>, <i>Indeed</i>, <i>Facebook</i>, <i>Twitter</i> <i>and</i> <i>Instagram</i>.</p>
<p><b>
Position Summary:</b><br>
We have an exciting opportunity to join our team as a Engineer II - Applied AI.
</p><p>As a ML Engineer, you will support the implementation of diverse Generative AI and Machine Learning initiatives across the health system. You will be responsible for driving specific projects forward in collaboration with the key customers - data scientists, business units and IT partners across the various verticals (corporate services, office of science and research, clinical, and pathology).</p>
<p><b>
Job Responsibilities:</b></p>
<div>
<ul><li>Design and implement AI/ML applications using tools and frameworks from our teams technology stack and following the best practices in software development</li>
</ul></div><div><ul><li>Collaborate with data scientists to build and / or fine-tune high performing AI models and implement modern techniques from relevant published papers</li>
</ul></div><div><ul><li>Develop scalable and robust pipelines to support large data processing tasks</li>
</ul></div><div><ul><li>Implement our internal security and data protection standards in AI applications; assist the team with maintaining emerging AI system risk and security standards</li>
</ul></div><div><ul><li>Support the team in preparation for technical reviews and internal documentation (architecture, IT Security, AI)</li>
</ul></div><div><ul><li>Collaborate with other team members and stakeholders to meet team objectives; develop understanding of business workflows shadow / work with clients to gather feature requirements, identify and document AI opportunities, create appropriate tickets, participate in backlog refinement, execute tickets, participate in code-review activities</li>
</ul></div><div><ul><li>Stay updated with the latest industry trends and advancements in AI and relevant cloud technologies; routinely share and demonstrate learnings with the team</li>
</ul></div><div><ul><li>Provide technical guidance and coaching to less experienced team members</li>
</ul></div><div><ul><li>Participate in all phases of AI software development life cycle, including functional analysis, prototyping, development, evaluation, testing, deployment, refactoring and technical support</li></ul></div><br>
<p>
</p><p><b>Minimum Qualifications:</b><br>
To qualify you must have a 1. Bachelor's degree in computer science, software engineering, or a related field.<br>
2. At least 1-3 years of hands-on experience in AI Solution development<br>
3. Strong programming skills in Python, or other languages commonly used in AI development.<br>
4. Substantial knowledge of AI, machine learning, and deep learning<br>
5. Experience with AI platforms like PyTorch or TensorFlow<br>
6. Experience with building large-scale and/or compute-intensive applications on clusters for data engineering, model training and evaluation (HPC, Spark, Kubernetes)<br>
7. Understanding of software development principles and methodologies, including data structures, data modeling and software architecture.<br>
8. Excellent problem-solving skills and ability to work in a team environment.<br>
9. Excellent communication skills, both verbal and written.</p>
<p><b>
Preferred Qualifications:</b><br>
1. Masters degree in computer science, data science, software engineering, or a related quantitative discipline<br>
2. 3-5 years of hands-on experience in AI Solution development<br>
3. Basic understanding of at least one cloud (Azure, AWS, GCP) and cloud native development tools, and experience using their AI / ML toolchain</p>
<p><i>
Qualified candidates must be able to effectively communicate with all levels of the organization.</i>
<br>
<br>
NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you'll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it's developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.
<br>
<br>
NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.
<br>
<br>
View Know Your Rights: Workplace discrimination is illegal.</p>
<p>
NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $84,577.92 - $126,000.00 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.</p>
<p>
To view the Pay Transparency Notice, please click here</p></div>

Machine Learning Engineer

Rilla

<div>
 <strong>
  A Foundational Company
  <br/>
  <br/>
 </strong>
 Rilla is on a mission to index the offline world.
 <br/>
 <br/>
 Today, we&rsquo;ve built the leading sales coaching software for organizations doing sales offline. We have over 1000 customers, including The Home Depot, KKR, Neighborly, and PulteGroup, using our product to enable AI sales coaching across their organization.
 <br/>
 <br/>
 We are backed by Google Ventures, Bessemer Ventures, Crew Capital, and Broom Ventures, along with others.
 <br/>
 <br/>
 We&rsquo;re an in office team in NYC with builders who operate like high speed reinforcement learners. We obsess over our customers, move at a high velocity, are not afraid of failure, and look to maximize our real-world impact.
 <br/>
 <br/>
 If you want to build the products and systems for how billions of conversations in the real world are captured and indexed, Rilla is the place to do it.
 <br/>
 <br/>
 <strong>
  The Role
  <br/>
  <br/>
 </strong>
 The Applied AI team at Rilla is redefining how humans interact with machines&mdash;using voice, intelligence, and a whole new class of data.
 <br/>
 <br/>
 Every day, you&rsquo;ll architect and ship AI-powered systems that make it possible for people to talk to Rilla like they would a human. You&rsquo;ll help build agents that operate natively on real-world audio, extract insights from conversations no one else can even access, and push the boundaries of what&rsquo;s possible in AI.
 <br/>
 <br/>
 <strong>
  We&rsquo;re Building
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   A voice-first interface that lets users command Rilla directly through natural speech
  </li>
  <li>
   A search engine that uncovers business-critical insights from voice data that&rsquo;s never been searchable
  </li>
  <li>
   A first-of-its-kind audio intelligence pipeline&mdash;designed for the messy, noisy, wildly unstructured conversations that happen in the real world, not in an online meeting
   <br/>
   <br/>
  </li>
 </ul>
 As an early AI engineer, you&rsquo;ll shape the foundations of our AI stack and drive the invention of new techniques, tooling, and models. You&rsquo;ll work across the full AI lifecycle&mdash;from data acquisition to real-time inference and user-facing chat interfaces.
 <br/>
 <br/>
 If you&rsquo;re obsessive about your craft, think like a founder, and want to be on the frontier of voice and offline AI, this is your team. The scale of the problems we&rsquo;re solving is unmatched&mdash;and the surface area for ownership is only getting bigger.
 <br/>
 <br/>
 <strong>
  Technologies We Use
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   We work primarily in Python and TypeScript, with FastAPI for API development
  </li>
  <li>
   AI/ML technologies including PyTorch, OpenAI APIs, Baseten, and LiteLLM
  </li>
  <li>
   Cloud infrastructure with AWS and LiveKit for real-time communications
  </li>
  <li>
   PostgreSQL, Redis, and S3 for data storage
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What We Value
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   An infinite learner. Someone who is relentless in their curiosity.
  </li>
  <li>
   A customer obsessive. Someone who deeply care about delighting customers and solving real pains, not vanity metrics.
  </li>
  <li>
   A winning attitude. You thrive with extreme accountability, learning as a team, and both giving and receiving rapid feedback.
  </li>
  <li>
   Extreme empathy. Our customers are not tech companies. They live and work in the real world. Empathy is critical to arriving at correct solutions.
  </li>
  <li>
   Unafraid of failure. You take risks. You see failure as an opportunity to learn, grow, and be better the next time. In a weird way, you trick your brain into being excited when you fail, because it means you got a new opportunity to learn more.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What We Require
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience building and deploying AI/LLM systems in production
  </li>
  <li>
   Familiarity with tools that power today's AI: eval frameworks, agent tooling, and prompt engineering
  </li>
  <li>
   Comfort working directly with customers to understand their needs and solve real-world problems
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Don&rsquo;t Work Here
  <br/>
  <br/>
 </strong>
 <strong>
  Please Don't Join If You're Not Excited About
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Building an AI product for an industry untouched by modern software
  </li>
  <li>
   Constantly talking to and visiting customers in the field
  </li>
  <li>
   Working ~60 hrs/week in person with some of the most ambitious people in NYC
  </li>
  <li>
   Attempting to build a generational company, and the intensity required to do so
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation
  <br/>
  <br/>
 </strong>
 The estimated salary range for this position is $200,000 - $300,000/year.
 <br/>
 <br/>
 Successful candidates with competing offers will receive matching and competitive offers.
 <br/>
 <br/>
 Total compensation for this position may also include Incentive Stock Options, sign-on bonus, and other potential future incentives. This estimate excludes the value of any potential sign-on bonus; the value of any benefits offered; and the potential future value of any long-term incentives.
 <br/>
 <br/>
 Our benefits aim to empower Rilla employees in removing roadblocks to achieving their life&rsquo;s work. The list below details our available benefits and some of the perks that can be enjoyed as an employee of Rilla.
 <br/>
 <br/>
 <strong>
  Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Medical, dental, and vision insurance
  </li>
  <li>
   Breakfast, lunch, dinner, and supplementary food and drink
  </li>
  <li>
   401(k) retirement plan
  </li>
  <li>
   Gym membership
  </li>
  <li>
   Commuter benefits
  </li>
  <li>
   Relocation assistance
  </li>
  <li>
   Take what you need paid time off, not accrual based
  </li>
  <li>
   $1,000 stipend for learning and personal growth
  </li>
  <li>
   $1,500 a month rent stipend
  </li>
  <li>
   Tech equipment
  </li>
 </ul>
</div>


Software Engineer, Applied AI

Software Engineer, Machine Learning RecSys

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