Machine Learning Experimentation

Machine Learning Experimentation Developer Jobs

5 jobs found

Today

Machine Learning Engineer

Reddit San Francisco, CA 🇺🇸 Remote

San Francisco, CA

Today

$230k - $322k/yr Full time Python Artificial Intelligence Data Analysis

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Job Description

Build and scale machine learning systems that power Reddit notifications across relevance, targeting, ranking, and personalization.

Responsibilities

Design, build, and maintain scalable ML infrastructure for notification relevance, targeting, ranking, and personalization across channels including push, email, and in-app notifications for millions of users.
Architect and implement end-to-end ML pipelines covering data ingestion, feature computation, model training, evaluation, deployment, and online serving in production.
Partner cross-functionally with data scientists, product managers, and engineers to define requirements, align on priorities, and deliver notification solutions end to end.
Lead development of experimentation frameworks (for example A/B testing, multi-armed bandits, or similar methods) to evaluate new notification models, ranking strategies, and targeting policies.
Mentor and provide technical guidance to engineers, sharing best practices in machine learning engineering, distributed systems, and ML infrastructure design.

Requirements

Bachelor's degree in Computer Science, Engineering (any field), or a related quantitative discipline.
Five (5) years of experience in the job offered or a related occupation.

Special Skill Requirements

Designing, building, and iterating large-scale scalable software systems.
Designing and building ML recommender systems at high scale.
Delivering large and complex systems with significant business impact.
Cross-functional collaboration on large-scale projects with complex dependencies across teams.
Object-oriented programming in Python, Golang, or Java.
Building ML models with PyTorch or TensorFlow.
API design and integration with REST, HTTP, Thrift, or gRPC.
Working with large-scale key-value and NoSQL storage and caching systems including Redis, Cassandra, and DynamoDB.
Working with large-scale messaging and event-driven systems including Apache Kafka, AWS SQS, AWS SNS, and SES.
Building workflows using orchestration systems including Kubeflow, Ray, Apache Airflow, and AWS Step Functions.
Working with large-scale analytics and data warehousing platforms including Google BigQuery and Amazon Redshift.
Experience with observability, logging, tracing, and monitoring tools such as Prometheus, Grafana, and AWS Cloudwatch.
Designing, running, and analyzing A/B experiments to measure and optimize system performance.

Technologies

Languages: Python, Golang, Java
ML Frameworks: PyTorch, TensorFlow
APIs/Protocols: REST, HTTP, Thrift, gRPC
Data Stores/Caching: Redis, Cassandra, DynamoDB
Streaming/Messaging: Apache Kafka, AWS SQS, AWS SNS, SES
ML/Workflow Orchestration: Kubeflow, Ray, Apache Airflow, AWS Step Functions
Analytics/Warehousing: Google BigQuery, Amazon Redshift
Observability: Prometheus, Grafana, AWS Cloudwatch

Location

San Francisco, CA (remote)
Full-time telecommuting is an option

Salary

USD 230,000 - 322,000 per yearly
Base pay range for this position: $230,000.00 - $322,000.00 USD

Benefits

Comprehensive Healthcare Benefits and Income Replacement Programs
401k with Employer Match
Global benefit programs: workspace to professional development to caregiving support
Family Planning Support
Gender-Affirming Care
Mental Health & Coaching Benefits
Flexible Vacation & Paid Volunteer Time Off
Generous Paid Parental Leave

Pay Transparency

This job posting may span more than one career level.
Eligible for equity in the form of restricted stock units (RSUs).
Depending on the position offered, eligibility for commission may apply.
Reddit offers benefits to U.S.-based employees including medical, dental, vision insurance, 401(k) with employer match, generous time off, and parental leave.
#LI-DNI

Interview Recording Notice

In select roles and locations, interviews will be recorded, transcribed, and summarized by artificial intelligence (AI).
You can opt out of recording, transcription, and summarization prior to scheduled interviews.
During the interview, Reddit will collect categories of personal information including identifiers, professional and employment-related information, sensory information (audio/video recording), and any other personal information you choose to share.
Information is used to evaluate your application for employment or an independent contractor role, as applicable.
Reddit will not sell personal information or disclose it to third parties for marketing purposes.
Reddit will delete any recording promptly after making a hiring decision.
For more information, refer to the Candidate Privacy Policy for Potential Employees and Contractors.

Equal Opportunity

Reddit is an Equal Opportunity Employer committed to building a workforce representative of the diverse community.
Reasonable accommodations are available for qualified individuals with disabilities and disabled veterans in the job application procedures.
If you need an accommodation during the interview process due to a disability, let your recruiter know.

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12d ago

Machine Learning Engineer, Core Experimentation

OpenAI Seattle, WA

Seattle, WA

12d ago

$437k - $485k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

OpenAI’s Statsig team is building the experimentation and analytics foundation that helps the company ship new product capabilities with speed, safety, and evidence. In this Machine Learning Engineer role, you will guide the technical direction for ML-powered experimentation and insights, shaping production systems that produce traceable, calibrated, privacy-protected evidence to support high-stakes decisions.

You will work end-to-end, from early prototypes through production adoption, helping teams move from model predictions to experimentation-backed understanding. Live experiments remain the source of causal validation, and the systems you create will make uncertainty explicit while supporting review, permissions, and approval boundaries as automation increases.

Role focus

Set and execute the technical roadmap for Generative Insights and Predictive Experimentation, moving from 0-to-1 prototypes to production use.
Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, and reanalyze prior results when data or methods improve.
Generate hypotheses with clear evidence and provenance, connecting offline insights to what teams can verify in live experiments.
Develop predictive models and simulation workflows to estimate likely impact, affected segments, regression risk, and uncertainty before running full live experiments.
Create high-quality datasets and feature or retrieval pipelines using exposures, events, metrics, experiment metadata, and replay data, with strong lineage, freshness, privacy, and data-quality controls.
Establish rigorous evaluation using offline benchmarks, backtests, calibration, drift monitoring, prediction-to-outcome comparisons, and explicit failure or abstention behavior.
Turn models into durable product, API, and agent workflows that connect insight generation to experiment design, approval-gated action, and measured learning.
Partner closely with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and the boundary between prediction and causal evidence.
Build reliable services and intuitive workflows that help teams interpret sophisticated ML capabilities for real-world product decisions.
Provide technical leadership across engineering.

Requirements

Experience leading ambiguous 0-to-1 production ML products where success is measured by better real-world decisions, not only offline metrics.
Hands-on capability across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
Depth in one or more areas including LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
Strong software engineering fundamentals and ability to build high-quality production systems in Python while working comfortably across data boundaries.
Strong grounding in machine learning study or equivalent practical experience.
Understanding of experimentation and statistical reasoning, especially why predictive accuracy is not the same as causal validity.
Treat calibration, uncertainty, provenance, privacy, and human review as product requirements.
Ability to translate ambiguous partner questions into a product and technical roadmap, collaborating well across product, data science, research, and infrastructure.
Comfort building for internal power users and agents, making sophisticated ML capabilities clear and actionable.
Value in-person collaboration and interest in helping shape a growing Bellevue-based team.

Technologies

Python
LLM
Retrieval systems

About the team

The Statsig team within OpenAI builds experimentation, feature rollout, dynamic configuration, and analytics systems to help OpenAI ship products with speed, safety, and evidence.
Statsig began as an independent company focused on trustworthy experimentation and feature management.
Today, the team supports capabilities across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and shared infrastructure that connects them.
Teams rely on Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence.
The role is on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions.

Location and workplace

Based in Seattle, WA (onsite).
The team works in person to move quickly, solve ambiguous problems together, and stay close to the product teams it supports.

Compensation

$437K to $485K per year + Offers Equity.
Base pay may vary based on individualized factors, and additional compensation and benefits may apply for eligible employees.

Benefits

Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts.
Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit).
401(k) retirement plan with employer match.
Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks).
Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees.
13+ paid company holidays, plus multiple paid coordinated company office closures throughout the year for focus and recharge, and paid sick or safe time as required by applicable state or local law.
Mental health and wellness support.
Employer-paid basic life and disability coverage.
Annual learning and development stipend.
Daily meals in offices, and meal delivery credits as eligible.
Relocation support for eligible employees.
Additional taxable fringe benefits may be provided, including charitable donation matching and wellness stipends.

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15d ago

Principal Machine Learning Engineer, Matching and Recommendations

Bumble Inc. New York, NY

New York, NY

15d ago

$345k - $410k/yr Full time Ai Ml Artificial Intelligence Big Data

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Job Description

At Bumble Inc., this Principal Machine Learning Engineer role focuses on defining and leading the technical direction for matching and recommendation systems that shape how people discover and connect. You’ll help drive measurable improvements across retrieval, ranking, personalization, experimentation, and marketplace optimization while staying hands-on throughout the full ML lifecycle.

This is a New York, NY onsite position, designed for senior engineers who can operate as an independent technical leader, influence strategy without direct authority, and champion responsible AI practices from model design through deployment.

What you’ll do

Define and lead the technical strategy for AI and machine learning systems that power recommendations, ranking, and personalization across Bumble products, improving both user engagement and safety
Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch, with a focus on scalability and reliability in high-traffic environments
Build and deploy production AI Agents that use raw and fine-tuned Large Language Models (LLMs), including sub-agents, tools, and MCP integrations
Architect end-to-end ML pipelines, integrating data processing tools such as Spark and Airflow with training, evaluation, and deployment workflows
Drive experimentation frameworks using A/B testing and offline evaluation to continuously improve model performance and product outcomes
Partner cross-functionally with Product, Engineering, and Data leadership to translate business challenges into impactful ML solutions
Mentor and elevate senior individual contributors, supporting a culture of Excellence, Curiosity, and continuous learning across the ML community
Own complex and ambiguous problem spaces from insight to impact, adapting approaches with an agile mindset
Champion responsible AI practices by embedding fairness, transparency, and user safety into machine learning systems

What you bring

Typically 10–15 years of experience, with openness to alternative backgrounds that demonstrate equivalent skills
Deep expertise in machine learning, including hands-on experience building and deploying large-scale production systems
Strong proficiency in Python and at least one major ML framework such as PyTorch or TensorFlow, with experience across areas like recommendation systems, ranking models, or NLP
Expertise in prompting and fine-tuning LLMs, plus experience building production AI Agents
Proven ability to design scalable data and ML pipelines using tools such as Spark and Airflow (or similar distributed systems)
Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decisions without direct authority
Experience partnering effectively across functions and taking ownership of outcomes in complex environments
A track record of mentoring others, role modeling Respect and Excellence, and building inclusive, high-performing teams
Strong AI fluency to independently design, evaluate, and optimize ML systems and guide others in responsible, effective AI use

Technologies you’ll work with

Python, PyTorch, TensorFlow
Large Language Models (LLMs), MCP
Spark, Airflow
A/B testing

Compensation

USD 345,000 - 410,000 per year
Final compensation will be determined based on qualifications, relevant experience, skill set, and other job-related considerations

Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and encourages people of all ages, color, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply
Reasonable adjustments are available to help you feel more confident throughout the process; you can share what you need
You can include your pronouns in your application (for example, she/her, he/him, they/them)

How Bumble may use AI in the hiring process

Bumble may use AI tools to support parts of recruitment such as recording, transcribing, summarizing conversations, and aligning job requirements by comparing resumes and job descriptions to highlight skills and potential matches
All hiring decisions are made by people; AI is used only to support efficiency and improve candidate experience, not to evaluate or decide on candidacy
Participation in AI-supported interviews and conversations is voluntary and does not affect candidacy
If you prefer to opt out, tell your recruiter or interviewer at the start of a call or anytime during the interview or conversation
Summaries and related data are retained only as long as needed under internal retention policies, and you can request deletion by contacting your recruiter

Fraudulent candidate detection

Bumble’s applicant tracking system analyzes signals related to device, IP, email, and phone data to protect applicants and the hiring process
These signals are used only for internal review and are not a definitive determination of identity or intent
No applicant is rejected, advanced, or otherwise affected based solely on this analysis without human review

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20d ago

Machine Learning Engineer, Applied Research

Whatnot New York, NY

New York, NY

20d ago

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

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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

Senior Machine Learning Engineer

Shipt Remote 🌍 Remote

Remote

1mo ago Closed

Full time Artificial Intelligence Data Analysis Engineer

This position is no longer accepting applications (closed Sep 22, 2026).

Job Description

Shipt’s Foundations team is hiring a Senior Machine Learning Engineer to build and deliver advanced machine learning models and to drive rigorous experimentation across pricing-related challenges. This role blends modeling and experimentation expertise, with responsibility for validating how algorithmic changes affect key business metrics.

You will operate as a subject matter expert in a machine learning and experimentation domain, partnering closely with product, business, and engineering stakeholders to turn hypotheses into measurable outcomes.

Responsibilities

Implement cutting-edge machine learning models
Design and analyze advanced experiments in collaboration with product, business, and engineering teams to address key challenges across multiple pricing domains at Shipt
Serve as the machine learning and experimentation subject matter expert in a defined domain
Build advanced ML solutions and validate the impact of algorithmic changes on key business metrics
Present recommendations to key stakeholders

Requirements

4+ years of experience applying machine learning and statistics in an industry setting, with a track record of delivering impactful results
PhD or Masters in Computer Science, Statistics, or a related field
Experience building and deploying machine learning models
Experience with experiment design, rollout, analysis, and reporting
4+ years of Python development
Experience presenting to stakeholders
Experience leading key technical projects and substantially influencing the scope and output of others
Bachelor’s Degree or equivalent experience

Technologies

Python
Artificial Intelligence (AI)
Machine Learning (ML)

Benefits

Medical, dental, vision and more
401k plan eligibility
Discretionary vacation for exempt team members
Paid holidays throughout the calendar year
Paid sick leave
Eligibility for an annual bonus
Potential for restricted stock units based on role

Work Arrangement

Remote roles are listed as Remote.
Hybrid roles in Birmingham, AL typically work in-office at least 2 days per week, with core in-office days on Wednesdays and Thursdays.
Hybrid roles in Minneapolis, MN typically work in-office at least 1 day per week on either Tuesday, Wednesday, or Thursday.
Hybrid roles in San Francisco, CA typically work in-office at least 1 day per week between Monday and Thursday.
Certain roles may require in-office presence on a full-time basis.

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Machine Learning Experimentation Developer Jobs

Machine Learning Engineer

Machine Learning Engineer, Core Experimentation

Principal Machine Learning Engineer, Matching and Recommendations

Machine Learning Engineer, Applied Research

Senior Machine Learning Engineer

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