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machine learning engineer

deep learning engineer

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2>
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. 
 
Key job responsibilities 
Participate in the Science hiring process as well as mentor other scientists - improving their skills, their knowledge of your solutions, and their ability to get things done. 
 
Identify and devise new video related solutions following a customer-obsessed scientific approach to address customer or business problems when the problem is ill-defined, needs to be framed, and new methodologies or paradigms need to be invented at the product level.
 
 
Articulate potential scientific challenges of ongoing or future customers&rsquo; needs or business problems, and present interventions to address them. 
 
Independently assess alternative video related technologies, driving evaluation and adoption of those that fit best
 
 
A day in the life 
As an Applied Scientist on the Sponsored Brands Video team, you will work with a team of talented and experienced engineers, scientists, and designers to help bring new products to market and ensure that our customers are delighted by what we create. The Sponsored Brands Video team is responsible for the design, development, and implementation of Sponsored Brands Video experiences worldwide. 
 
About the team 
The Sponsored Brands Video team within Sponsored Products and Brands creates relevant and engaging video experiences, connecting advertisers and shoppers. We are on a mission to make Amazon the best in class destination for shoppers to discover, engage and build affinity with brands, making shopping delightful, &amp; personal.<h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>3+ years of building machine learning models for business application experience</li><li>PhD, or Master's degree and 6+ years of applied research experience</li><li>Experience programming in Java, C++, Python or related language</li><li>Experience with neural deep learning methods and machine learning</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.</li><li>Experience with large scale distributed systems such as Hadoop, Spark etc.</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, WA, SEATTLE - 167,100.00 - 226,100.00 USD annually</div>

Applied Scientist III, Sponsored Products

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

Nebius

<div><div>About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&amp;D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&amp;D.</div><div>

About Tavily 
 
We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.

We work with some of the most innovative teams in AI - from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.

Role:
</div>
We are looking for a Senior Business Systems Engineer to own our GTM Operations systems and strategy. You will have an integral role in architecting the engine that powers Tavily's growth. You will be our first hire to this team, building a seamless, scalable GTM stack and ensuring our data architecture supports world-class execution across Sales, Marketing, and Finance.
<ul>
<li>Own the end-to-end technical architecture of the GTM tech stack, ensuring seamless data flow across the entire customer lifecycle&mdash;from initial marketing lead to sales conversion and customer success renewal. We will be implementing a fresh SFDC instance which you will help create from the ground up.</li>
</ul><ul><li>Serve as the primary technical liaison for Sales, Finance, and Marketing leadership, translating complex departmental requirements into a multi-quarter technical roadmap for the Force.com platform and integrated ecosystem.</li>
</ul><ul><li>Establish and enforce rigorous engineering standards, including automated testing and security protocols to ensure the reliability and integrity of internal GTM applications.</li>
</ul>
What We're Looking For (Must Have):
<ul>
<li>6+ years of Force.com development experience with a deep understanding of Apex, LWC, and platform limits.</li>
</ul><ul><li>Proven track record of designing complex enterprise integrations and managing high-volume data pipelines.</li>
</ul><ul><li>BS/MS in Computer Science, Computer Engineering, or a related technical field.</li>
</ul><ul><li>Ability to navigate complex relational databases and leverage data for strategic decision-making.</li>
</ul><ul><li>Experience with Agile methodologies and managing a prioritized backlog in Jira or similar tools.</li>
</ul>
Nice to have:
<ul>
<li>Salesforce Certified Application or System Architect.</li>
</ul><ul><li>Deep experience with CPQ and Billing, Stripe, or Netsuite integrations.</li>
</ul><ul><li>Advanced expertise in Integration Platforms like Workato, Zapier or Mulesoft.</li>
</ul><div>
Benefits &amp; Perks:
<ul>
<li>Competitive compensation</li>
<li>Career growth and learning opportunities</li>
<li>Flexibility and ownership</li>
<li>Collaborative and innovative culture</li>
<li>Opportunity to work on impactful AI projects</li>
<li>International environment and talented teams</li>
</ul>
What's it like to work at Nebius:

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

Equal Opportunity Statement:

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

If you need accommodations during the application process, please let us know.</div></div>

Senior Business Systems Engineer (Agentic Search) - Tavily

Achira

<div><h1 class="jobSectionHeader">Why Achira</h1>
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you&rsquo;ll operate at the frontier scale of massive compute, massive data, and massive ambition. You&rsquo;ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We&rsquo;re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.<h1 class="jobSectionHeader">
About the Role</h1>
We&rsquo;re looking for deep learning researchers who want to build rich representations of atomistic systems to help teach AI about the microscopic world. You will collaborate with domain experts in molecular modeling to develop Achira&rsquo;s next-generation foundation models. You&rsquo;ll work at the intersection of model architecture, data, and training strategy to find new points on the Pareto frontier of accuracy and speed for microscale world models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.<h1 class="jobSectionHeader">
What You&rsquo;ll Do</h1><ul><li>
Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact.</li><li>
Create reenforcement learning strategies to help models focus their capacity where it matters most, especially when the training data doesn&rsquo;t cover the domain of applicability.</li><li>
Develop expressive representations of molecular and atomistic structure and dynamics, including equivariant graph neural networks, geometric transformers, and latent encoders that capture physical symmetries and constraints.</li><li>
Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira&rsquo;s ecosystem.</li><li>
Collaborate with physicists and chemists to ensure models are grounded in real physics.</li><li>
Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.</li></ul><h1 class="jobSectionHeader">
About You</h1><ul><li>
Drive to apply modern ML techniques to solve problems at the frontier of the microscopic world.</li><li>
A willingness to follow the data and embrace an empirical approach to the bitter lesson.</li><li>
A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building.</li><li>
Experience designing, running and analyzing ML experiments at scale.</li><li>
Experience with 3D geometric deep learning.</li><li>
Machine learning researcher with professional experience (post-degree) in an industry setting.</li><li>
Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.</li><li>
Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.</li><li>
Proficiency in Python and modern ML frameworks (PyTorch, JAX).</li><li>
Experience collaborating on research projects across multi-person teams.</li></ul><h1 class="jobSectionHeader">
Nice to Have</h1>
Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!<ul><li>
Experience with self-supervised representation learning techniques.</li><li>
Experience with Bayesian deep learning techniques and uncertainty quantification.</li><li>
Experience developing or applying generative models for 3D systems.</li><li>
Experience with equivariant graph neural network architectures (NequIP, MACE, SchNet, PaiNN, or similar).</li><li>
Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.</li><li>
Experience working with multi-cloud distributed compute systems.</li><li>
Experience working with multi-site distributed company team.</li></ul>
Compensation Range: $164,638 - $259,000</div>

ML Research Scientist (MLRS) - Representation Learning for Molecular AI

Tavily

<div><div>About Tavily 
 
We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.

We work with some of the most innovative teams in AI - from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.

Role:
</div>
We are looking for a Senior Business Systems Engineer to own our GTM Operations systems and strategy. You will have an integral role in architecting the engine that powers Tavily's growth. You will be our first hire to this team, building a seamless, scalable GTM stack and ensuring our data architecture supports world-class execution across Sales, Marketing, and Finance.
<ul>
<li>Own the end-to-end technical architecture of the GTM tech stack, ensuring seamless data flow across the entire customer lifecycle&mdash;from initial marketing lead to sales conversion and customer success renewal. We will be implementing a fresh SFDC instance which you will help create from the ground up.</li>
</ul><ul><li>Serve as the primary technical liaison for Sales, Finance, and Marketing leadership, translating complex departmental requirements into a multi-quarter technical roadmap for the Force.com platform and integrated ecosystem.</li>
</ul><ul><li>Establish and enforce rigorous engineering standards, including automated testing and security protocols to ensure the reliability and integrity of internal GTM applications.</li>
</ul>
What We're Looking For (Must Have):
<ul>
<li>6+ years of Force.com development experience with a deep understanding of Apex, LWC, and platform limits.</li>
</ul><ul><li>Proven track record of designing complex enterprise integrations and managing high-volume data pipelines.</li>
</ul><ul><li>BS/MS in Computer Science, Computer Engineering, or a related technical field.</li>
</ul><ul><li>Ability to navigate complex relational databases and leverage data for strategic decision-making.</li>
</ul><ul><li>Experience with Agile methodologies and managing a prioritized backlog in Jira or similar tools.</li>
</ul>
Nice to have:
<ul>
<li>Salesforce Certified Application or System Architect.</li>
</ul><ul><li>Deep experience with CPQ and Billing, Stripe, or Netsuite integrations.</li>
</ul><ul><li>Advanced expertise in Integration Platforms like Workato, Zapier or Mulesoft.</li></ul></div>

Senior Business Systems Engineer (Agentic Search)

Doppel

<div>
 
 Why Join Doppel
 
 
 
 Doppel is building the future of social engineering defense. Our AI-native platform uses agentic AI to protect executives, employees, customers, and brands from phishing, impersonation, fraud, and other AI-powered threats across digital channels. We help some of the world&rsquo;s most recognized brands detect and dismantle attacker infrastructure while strengthening employee resilience through threat-informed training and simulation. By unifying Digital Risk Protection, Human Risk Management and Email Security, Doppel connects threats into a real-time intelligence graph to power faster disruption, smarter defense, and modern security awareness at scale.
 
 
 Backed by leading investors including Andreessen Horowitz and Bessemer Venture Partners, and trusted by leading enterprises, Doppel is a rapidly growing Series C startup building the future of social engineering defense. Our team combines deep cybersecurity expertise, operational rigor, and startup velocity to solve some of the internet&rsquo;s most urgent trust and safety challenges.
 
 
 
 What We're Building
 
 
 
 We're building the AI-native social engineering defense platform.
 
 
 This means we're designing scalable systems that monitor billions of domains, social media accounts, apps, dark web forums, etc., and leverage AI agents to identify and neutralize digital threats.
 
 
 
 What We're Looking For
 
 
 
 We&rsquo;re looking for an experienced backend engineer to build out the infrastructure required to rapidly scale up our engineering organization. A few examples of the work we&rsquo;ve done recently:
 
 
 <ul>
 <li>
 Built a scalable data platform with a robust DBT transformation layer, enabling customers, engineers, and analysts to reliably model and query large-scale event data, track key product metrics, and power customer facing analytics.
 </li>
 <li>
 Launched a flexible notification system, delivering timely, reliable alerts across Slack, email, and other channels for internal and customer-facing use cases.
 </li>
 <li>
 Standardized core service patterns and shared business logic (auth, persistence, messaging), scaling them into reusable services that reduce duplication, ensure consistency, and enable product engineers to move faster as the company grows.
 
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 🚀 A mission-driven culture with low ego, high ownership, deep customer obsession, and exceptional talent density
 
 
 🍽️ Free lunch and dinner in the office
 
 
 🌴 Flexible PTO
 
 
 ✈️ Quarterly team offsites
 
 
 Join Doppel
 
 
 Doppel is the first platform built to dismantle digital deception at scale. We scan over 150 billion entities daily and deploy continuously adaptive AI SOC agents, paired with expert human analysts, to uncover and disrupt the infrastructure behind phishing, impersonation, and online fraud before attacks can spread. Our Threat Grid turns every customer signal into shared intelligence, making each disruption smarter, faster, and more effective.
 
 
 We&rsquo;re not just another cybersecurity company. We&rsquo;re defining the future of social engineering defense, where trust is protected, and deception becomes unprofitable. Backed by top-tier investors and trusted by some of the world&rsquo;s most recognized brands, Doppel is growing fast. If you&rsquo;re driven to solve real-world problems with bold technology, we&rsquo;d love to meet you.
</div>

Software Engineer, Platform

Databricks

<div><h1 class="jobSectionHeader">Staff Machine Learning Engineer, CustomerLake (ML/LLM)</h1>
RDQ427R109 
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. 
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. 
The impact you'll have:
<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> 
What we look for:
<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> 
Nice to have:
<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> 
<div><div><div>Pay Range Transparency
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. 
 
</div><div>Local Pay Range</div>
<div>$192,000&mdash;$260,000 USD
</div></div></div><div>About Databricks
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.
 
 
Benefits
 
 
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. 
 
 
Our Commitment to Diversity and Inclusion
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.
Compliance
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.</div></div>

Staff Software Engineer - AI/ML

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