Senior Machine Learning Engineer

Senior Applied Scientist

The Core Recommendation Ranking team in Microsoft AI Content Org powers the end-...

Location

United States , Redmond

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)
OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.
4+ years of industry experience in applied science, machine learning, or deep learning at scale
Solid foundation in recommendation systems, ranking models, or search relevance
Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and cloud-scale ML infrastructure
Proficiency in Python and data processing tools (Spark, Pandas, or equivalent)
Track record of shipping ML models to production with measurable user impact
Experience with LLM-based ranking, retrieval-augmented generation (RAG), or generative recommendation systems

Job Responsibility

Design & implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques
Own end-to-end ML pipelines — feature engineering, model training, offline/online evaluation, and production inference optimization
Innovate by applying state-of-the-art methods including LLM-enhanced ranking, contextual bandits, reinforcement learning, and generative recommendation approaches
Collaborate cross-functionally with engineering, product, and platform teams to translate research insights into shipped features
Contribute to technical direction within the team — propose experiments, identify opportunities, and drive projects from ideation to production
Mentor less experienced scientists and engineers, fostering a culture of technical excellence and knowledge sharing

Fulltime

Senior Applied Scientist

In Feeds and AI team, we’re building Microsoft Start, a content powerhouse for e...

Location

China , Beijing

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Minimum of 3 years of professional experience in recommender systems, search engines, or online advertising, with strong expertise in machine learning algorithms, generative AI and large language models (LLMs), statistics, and data mining techniques applied to personalization.
Proven problem-solving abilities and a track record of thriving in fast-paced, collaborative environments.
Excellent communication and teamwork skills, with the ability to clearly articulate complex technical concepts to a variety of audiences.
Proficient in Python programming, with additional experience in languages such as C# or C++ considered a plus.

Job Responsibility

Algorithm Development and Enhancement for ranking algorithms in News & Feeds
Work with cross-functional teams to design, develop, and implement recommendation algorithms to deliver product features and drive user engagement.
Optimize existing recommendation algorithms by analyzing performance metrics and user feedback, incorporating advanced machine learning techniques including generative AI techniques.
Innovation in the area of advanced AI solution, and recommender system.
Perform data analysis to identify patterns, trends, and opportunities to improve the relevance and quality of our recommendation systems.
Build systemic solutions and models to optimize user experience.

Fulltime

Join us in building the future of finance. Our mission is to democratize finance...

Location

United States , Menlo Park, CA; Bellevue, WA

Salary:

209000.00 - 245000.00 USD / Year

Robinhood

Expiration Date

Until further notice

Requirements

5+ years of applied ML experience productionizing ML models with 2+ years focused on recommendations, ranking or personalization projects
A fervent interest in exploring and applying AI and ML technologies
Strive to solve sophisticated engineering problems that drive business objectives
Solid technical foundation enabling active contribution to the design and execution of projects and ideas
Familiarity with architectural frameworks of large, distributed, and high-scale ML applications
Hands-on experience in classical ML techniques with tabular data as well as modern techniques with sequential data
Proven experience in ML with a focus on ranking, recommendation systems, multi-objective optimization, and reinforcement learning
Proficiency in Python, SQL, XGboost, PyTorch/TensorFlow
Experience with Spark, Kafka, and Kubernetes is also desirable
Ideally you have experience in the Finance sector

Job Responsibility

AI and ML Research: Evaluate cutting technologies, including but not limited to, transformer based model architecture and large foundational models to identify solutions for Robinhood specific problems
Model Development and Implementation: Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content-Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation
A/B Testing and Experimentation: Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results
Data Analysis and Insight Generation: Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy
Cross-Functional Collaboration: Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders
Tooling and Documentation: Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings

What we offer

Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
100% paid health insurance for employees with 90% coverage for dependents
Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more
Exceptional office experience with catered meals, events, and comfortable workspaces

Fulltime