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Today

Machine Learning Engineer, Applied Research

Whatnot New York, NY

New York, NY

Today

$207k - $290k/yr Full time Artificial Intelligence Data Science Engineer

Apply Now

Job Description

Hybrid in New York, NY (within commuting distance of the NYC hub), this role leads applied research and machine learning work that turns marketplace hypotheses into production systems. You will help improve how experiments are evaluated and how marketplace decisions are made, supporting better simulation, allocation, and long-term outcome modeling across a multi-sided platform.

What you’ll do

Lead applied research projects focused on marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods.
Move ideas from hypothesis to production by running literature reviews, building prototypes, performing offline validation, conducting shadow testing, and shipping online experiments through partner teams in Discovery and Seller.
Build system-level models of how the marketplace behaves, including learned simulators to predict segment-level effects of ranking and policy changes and surrogate models for long-term marketplace outcomes.
Model Whatnot’s real market mechanics, such as auction and bidding dynamics and discovery exposure allocation treated as a portfolio problem, including allocation to rising sellers.
Advance evaluation of changes in a live multi-sided marketplace using off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction.
Strengthen Whatnot’s external technical presence through publications, open-source contributions, and public benchmarks.

What you bring

5+ years of industry experience building and deploying ML models to solve user problems at scale.
Deep experience in at least one area: recommendation systems, causal inference, off-policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation.
A demonstrated track record of applying scientific methods to real-world problems using consumer-scale data.
Advanced proficiency in Python, SQL, and common ML frameworks such as PyTorch and XGBoost.
Strong grounding in applied statistics, experiment design, and theoretical machine learning.
Strong communication and leadership, including the ability to influence roadmaps and align cross-functional teams in a remote environment.
Preferred: experience in two-sided marketplaces, ads and auction systems, or pricing.
Preferred: experience building simulators or economic models of platform behavior.

Tools you’ll use

Python
SQL
PyTorch
XGBoost

Compensation

$207K - $290K per year (full-time salary), plus benefits and equity for eligible US-based applicants: $207,000/year to $290,000/year + benefits + equity.

Benefits and support

Flexible Time Off Policy and company-wide holidays, including spring and winter break.
Health Insurance options: Medical, Dental, Vision.
Work From Home Support and home office setup allowance.
Monthly allowances for cell phone and internet and wellness.
Annual allowance towards Childcare.
Lifetime benefit for family planning, such as adoption or fertility expenses.
Retirement: 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally.
Monthly allowance to dogfood the app.
Parental Leave: 16 weeks of paid parental leave plus one month gradual return to work.

Location

New York, NY (Hybrid). Team members in this role are required to be within commuting distance (50 miles) of the New York City hub.

Security note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, disregard and report it as spam.

EOE: Whatnot is an Equal Opportunity Employer.

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1mo ago

Machine Learning Engineer I

Indeed Remote 🌍 Remote

Remote

1mo ago Closed

$118k - $245k/yr Full time Agent Orchestration Agentic Systems AI

This position is no longer accepting applications (closed Sep 16, 2026).

Job Description

Join Indeed’s Sourcing team to build and deploy ML and AI systems that improve automated outreach quality and support agentic experiences with dependable LLMOps infrastructure.

Responsibilities

Develop a variety of machine learning models from prototype to production
Implement best practices for evaluation, monitoring, and MLOps
Collaborate with cross-functional teams on model inference and product applications for the Sourcing product
Participate in code review and process innovation with software engineers and Machine Learning Engineers
Document work and contribute to team knowledge sharing
Apply technical and product thinking, with ownership of team priorities and Indeed’s mission to help people get jobs

Requirements

Education: Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field
Experience: minimum 2 years of related experience; or an advanced degree without experience
Familiarity with agent orchestration frameworks and LLM observability tools
Significant domain knowledge covering training, fine-tuning, and evaluating language models
Practical experience working with ML/LLM/Deep Learning models (examples include Torch, Tensorflow, Huggingface, XGBoost, etc.)
Python and SQL programming experience

Technical skills / technologies

Python
SQL
Torch
Tensorflow
Huggingface
XGBoost
LLM observability tools
Agent orchestration frameworks

Compensation

Salary range: USD 118,000 to 245,000 per year
Tier 1 (United States of America): $118,000 - $176,000 USD per year
Tier 2 (United States of America): $130,000 - $196,000 USD per year
Tier 3 (United States of America): $143,000 - $215,000 USD per year
Tier 4 (United States of America): N/A
Tier 5 (United States of America): $163,000 - $245,000 USD per year

Salary range transparency disclaimer: The salary range reflects minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation is determined based on job-related skills, experience, and expertise during the interview process. The range(s) listed are one component of Indeed’s total compensation package; other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and region-specific benefits. Compensation may vary by location and geographic pay tiers. Candidates can view geographic pay tiers at https://www.indeed.com/careers/paytiers, and recruiters can confirm how location is considered for a specific role.

Benefits

Medical, dental, vision, disability, and life insurance
Enrollment in the company’s 401k plan
Equity-based incentive program
Open paid time off
11 paid holidays a year
Up to 26 weeks of paid parental leave
Paid time off policy

Location

Remote (remote)

Additional info

Reference ID: 47241
Apply by: 8/17/2026
Job postings may be extended at the hiring team’s discretion based on applicant volume

AI notice: Indeed uses artificial intelligence (AI) tools to assist in screening, assessment, and selection of applicants for this position by analyzing information provided in resumes and applications. Human decision-making is not replaced by AI. Unless otherwise notified, Indeed does not use AI constituting an AEDT or an ADMT as defined in applicable laws.

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1mo ago

Sr. Lead Machine Learning Engineer

Capital One New York, NY

New York, NY

1mo ago Closed

$251k - $286k/yr Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

Capital One is hiring a Sr. Lead Machine Learning Engineer for an Agile team focused on productionizing machine learning applications at scale. This onsite role in New York, NY blends ML architecture, engineering, and responsible governance so teams can deliver real-world impact with dependable systems.

Compensation: USD 250,800 to 286,200 per year.

What you’ll do

Design, build, and/or deliver machine learning models and components that address business problems in collaboration with Product and Data Science teams.
Shape ML infrastructure decisions using hands-on expertise across model and data choices, including feature selection, training, hyperparameter tuning, dimensionality, and concepts like bias/variance and validation.
Develop and validate ML solutions through application code, testing, and test automation, while enabling smooth deployment of models and supporting software.
Work within a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production, supporting ongoing performance and reliability.
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful releases.
Ensure code quality and governance, reduce vulnerabilities, and support Responsible and Explainable AI best practices from a risk perspective.
Use programming languages including Python, Scala, or Java.

What you bring

Bachelor’s Degree.
8+ years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply).
4+ years programming with Python, Scala, or Java.
3+ years building, scaling, and optimizing ML systems.
2+ years experience leading teams developing ML solutions.
Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or similar.
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
4+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or XGboost.
3+ years developing performant, resilient, and maintainable code.
3+ years of data gathering and preparation for ML models.
3+ years of people management experience.
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
3+ years building production-ready data pipelines that feed ML models.
Ability to communicate complex technical concepts clearly to a variety of audiences.
Experience leveraging interactive AI tooling to accelerate productivity beyond basic code completion.

Skills and tools

Python, Scala, Java
AWS, Azure, Google Cloud Platform
scikit-learn, PyTorch, Dask, Spark, XGboost

Benefits

Comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.
Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

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1mo ago

Machine Learning Engineer, Growth

Whatnot Los Angeles, CA 🇺🇸 Remote

Los Angeles, CA

1mo ago Closed

$190k - $280k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

This position is no longer accepting applications (closed Sep 1, 2026).

Job Description

Whatnot offers a flexible, benefits-rich environment designed to accelerate growth, with a strong emphasis on product excellence and a collaborative culture. Based in Los Angeles with remote options, Whatnot is the largest live shopping platform in North America and Europe for buying, selling, and discovering a wide range of goods. Our distributed team operates across hubs in the United States, United Kingdom, Ireland, Poland, Germany, and Australia, moving quickly while staying closely connected to our users.

Benefits

Flexible time off policy and company-wide holidays, including spring and winter breaks
Comprehensive health coverage (medical, dental, and vision)
Support for remote work, plus a home office setup allowance
Monthly stipends for cell phone and internet
Care benefits and a monthly wellness allowance
Annual childcare allowance
Lifetime support for family planning, including adoption and fertility expenses
Retirement options with a 401(k) for the US (Traditional and Roth), employer match up to 4%; international pension plans
Monthly budget to dogfood the app, reinforcing product understanding
Parental leave of 16 weeks paid, plus a one-month gradual return to work (country requirements take precedence)
Equal opportunity employer focused on diversity and inclusive growth
All Whatnauts are encouraged to use Whatnot as buyers and sellers to deepen product insight

Responsibilities

Lead the design, development, and deployment of ML models that drive user acquisition, activation, and retention, including ranking, targeting, and uplift modeling
Own the full ML lifecycle: data pipelines, feature engineering, model training, deployment, and online experimentation; build feedback loops from postbacks, conversions, and downstream revenue signals
Identify high-impact growth opportunities and partner with product, engineering, data, and marketing to shape the roadmap
Promote technical excellence and establish ML best practices within the Buyer Growth team

Technologies

Python
SQL
PyTorch
TensorFlow
XGBoost

Requirements

5+ years building and deploying ML systems at scale
Proven track record of leading end-to-end ML projects with measurable business impact
Deep expertise in personalization, ranking, or user modeling, with strong intuition about consumer behavior
Advanced proficiency in Python, SQL, and common ML frameworks (PyTorch, TensorFlow, XGBoost)
Strong communication and leadership skills, with the ability to influence roadmaps and align cross-functional teams

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Xgboost Developer Jobs

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