Requirements:
A proven track record as an experienced architect who leads by example, capable of writing core, production-critical code, conducting rigorous architecture reviews, and mentoring senior technical staff.
At least 3 years of hands-on experience building, scaling, and optimizing Agentic LLM systems. This includes deep familiarity with common frameworks (e.g., LangChain, LangGraph, AutoGen) and a sophisticated understanding of tool-use optimization, memory management, and deterministic behavioral alignment.
At least 5 additional years of deep experience in data science, with expert-level command of classical machine learning algorithms applied to complex, real-world data landscapes.
At least 5 additional years of experience as a software engineer with high proficiency in Python and querying languages. You possess deep knowledge of software design patterns, concurrency, CI/CD pipelines, and writing clean, scalable, maintainable code.
BSc in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field from a top university. An advanced degree is highly preferred.
Demonstrated expertise in deploying and operating ML/LLM workloads at enterprise scale. You have a comprehensive understanding of containerization (Docker, Kubernetes), high-throughput data pipelines, and proactive system monitoring.
Exceptional ability to run end-to-end research-to-production initiatives, showcasing an analytical mindset capable of translating raw data into highly accurate, real-time defensive actions.
Extensive, hands-on experience utilizing, fine-tuning, and optimizing open-source generative AI frameworks and libraries (e.g., Hugging Face, PyTorch, vLLM, DeepSpeed) to build and deploy high-performance models.
Proven experience architecting data solutions and working within major cloud and big data ecosystems (e.g., GCP, AWS, Snowflake) to ingest, process, and analyze high-velocity, petabyte-scale datasets.

Requirements:
A proven track record as an experienced architect who leads by example, capable of writing core, production-critical code, conducting rigorous architecture reviews, and mentoring senior technical staff.
At least 3 years of hands-on experience building, scaling, and optimizing Agentic LLM systems. This includes deep familiarity with common frameworks (e.g., LangChain, LangGraph, AutoGen) and a sophisticated understanding of tool-use optimization, memory management, and deterministic behavioral alignment.
At least 5 additional years of experience as a software engineer with high proficiency in Python and querying languages. You possess deep knowledge of software design patterns, concurrency, CI/CD pipelines, and writing clean, scalable, maintainable code.
BSc in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field from a top university. An advanced degree is highly preferred.
Demonstrated expertise in deploying and operating ML/LLM workloads at enterprise scale. You have a comprehensive understanding of containerization (Docker, Kubernetes), high-throughput data pipelines, and proactive system monitoring.
Exceptional ability to run end-to-end research-to-production initiatives, showcasing an analytical mindset capable of translating raw data into highly accurate, real-time defensive actions.
Extensive, hands-on experience utilizing, fine-tuning, and optimizing open-source generative AI frameworks and libraries (e.g., Hugging Face, PyTorch, vLLM, DeepSpeed) to build and deploy high-performance models.
Proven experience architecting data solutions and working within major cloud and big data ecosystems (e.g., GCP, AWS, Snowflake) to ingest, process, and analyze high-velocity, petabyte-scale datasets.
Outstanding communication and presentation skills, with a demonstrated ability to align cross-functional engineering, product, and research teams toward unified technical goals

Requirements:
5+ years of hands-on experience with developing machine learning based solutions
Proven experience in delivering machine learning solutions to production at scale
Experience in working in a cloud environment
Proficient knowledge in Python (inc. Pandas, Numpy) and SQL
Familiarity with statistical modeling techniques
Experience in data analysis and visualization
Demonstrate strong problem solving and critical thinking skills
A proven record conducting end-to-end research
Experience in working as a member in a data science team
Preferred:
M.Sc. or PhD. in Computer Science, Mathematics, Engineering or a related field
Experience in developing models using deep learning techniques and frameworks
Experience with NLP techniques
Experience with e-commerce or marketing attribution.
Experience in Apache Spark.

Requirements:
4+ years of experience building machine learning solutions end-to-end, from research and experimentation through production.
MSc or PhD in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.
Experience developing machine learning models for real-time bidding (RTB), recommendation systems, ranking, pricing, or other large-scale optimization problems.
Strong programming skills in Python and experience working with production-grade machine learning systems.
Strong foundation in machine learning, statistical modeling, experimentation, and data analysis.
Bonus points if you have:
Experience with deep learning models in large-scale recommendation or advertising systems.
Experience using AI development tools (such as Claude Code or Cursor) to accelerate experimentation and development.

Requirements:
Minimum qualifications
5+ years of experience in leading data science and machine learning projects, with significant hands-on work building LLM-based applications or AI agents.
Deep practical experience with LLMs (prompt engineering, fine-tuning, model selection, and understanding trade-offs across providers and model families).
Knowledge of programming languages that are used in AI research, such as Python, and experience with AI frameworks (e.g., Hugging Face, LangChain, OpenAI, scikit-learn, TensorFlow, PyTorch).
An M.S. or Ph.D. degree in computer science, statistics, or related field OR equivalent work experience.
Preferred qualifications:
Experience with distributed cloud systems and hands-on familiarity with cloud-native architectures at scale.
Strong knowledge of deep learning models and common model architecture such as transformer models.
Knowledge of cybersecurity principles, attack vectors, and defense mechanisms.
Excellent communication (both written and verbal) and presentation skills.
Ability to work independently in a fast-paced environment and come up with creative solutions to challenging problems.

Requirements:
Education: MSc or PhD in Data Science, Computer Science, Mathematics, Statistics, or a related engineering field.
Experience: 4+ years of proven experience as a Data Scientist solving complex business problems with state-of-the-art algorithms.
Deep Learning: Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Keras).
Engineering & Production: High-level software engineering expertise, with a strong track record of deploying, maintaining, and scaling models in rigorous production environments.
Strong analytical thinking: Take a vague business question, translate it into a testable hypothesis, build the infrastructure to run an experiment, and draw rigorous conclusions from the results.
Massive Scale: Experience working with billions of records and big-data frameworks (e.g., Spark, Ray, Dask, Airflow).
Domain Expertise: Proven experience building and optimizing applied recommendation systems.
AI development: Fluency with modern AI coding tools and practices (e.g. Cursor, Claude Code, Copilot) and an eagerness to integrate them into your daily workflow
Leadership & Soft Skills: Demonstrated experience leading machine learning projects. You are a highly analytical team player capable of handling multiple tasks simultaneously in a fast-paced environment.
You might also have
LLM & Agentic Frameworks: Experience building applications with modern AI tools and frameworks (e.g., LangChain, LangGraph).
AI Observability: Hands-on experience with LLM observability, evaluation, and tracing frameworks (e.g., Langfuse).
MLOps & Infra: Experience working with MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases, SageMaker) and feature stores (e.g., Feast, Tecton).
Additional information
Relocation support is not available for this position
Work visa/immigration sponsorship is not available for this position
Benefits
At our company, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Requirements:
Master's degree in Electrical Engineering, Computer Science, Physics, or a related field

3+ years of experience in designing and implementing AI/ML algorithms, with a strong focus on signal processing

Proven experience working across the full ML lifecycle - from data acquisition and model training to production implementation and ongoing product iteration.

Moves fast, with strong engineering instincts, able to choose the right level of complexity to drive real-world impact

A get things done attitude with a strong sense of ownership

A team player with excellent communication and collaboration skills; able to convey complex ideas clearly to technical and non-technical partners

Exceptional analytical and problem-solving skills

A fast learner, willing to try things out of your comfort zone

Experience in speech, audio, or music-related domains - Advantage

Requirements:
Natural scientific curiosity and ability to handle complex problems.
At least 2 years of experience in hands-on Agentic LLM projects. This includes experience with the common agentic frameworks (e.g., LangChain, LangGraph), with emphasis on algorithmic design, large-scale deployment and monitoring.
At least 4 years of experience as a software engineer with high proficiency in python and at least good familiarity with SQL.
Deep understanding of probability, statistics, and machine learning models with a proven track record of selecting, modifying, and applying advanced algorithms to solve practical problems.
Demonstrated ability to run end-to-end research POCs, from conceptualization to solutions, including designing, executing, analyzing, and concluding research methodologies and outcomes.
Good communication skills, with the ability to convey complex research findings clearly and effectively to both technical and business audiences.
Preferred Qualifications
Advanced degree (MSc or PhD) in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field.
Background in the cybersecurity domain.
Hands-on experience with open-source generative libraries (e.g., Hugging Face).
Experience working with big data platforms (e.g., GCP).
Familiarity with cloud-native architectures and services for data processing.

Requirements:
Ph.D. in Computer science, Physics, Mathematics, computational biology or related field with 2+ years of industry experience, or an M.Sc. with 5+ years of relevant industry experience.

Strong proficiency in Python and PyTorch, with hands-on experience training and fine-tuning deep learning models on GPUs.

Strong algorithmic thinking and problem-solving skills, with the ability to translate theoretical ideas into practical solutions.

Strong communication and collaboration skills.

Fast learner and proven ability to master new domains.

Advantage: Proven experience developing and applying machine learning models to biological or chemical data. Experience with antibody modeling and design (sequence, structure, or interaction prediction).

Advantage: experience profiling and optimizing GPU workloads.

Requirements:
5+ years of experience in Machine Learning, working with tabular data on predictive modeling
Track record of translating research into business impact
MSc or PhD in a related field- Math/EE/CS/Physics/Stats
Great interpersonal skills, and strong collaborator
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Proficient in Python and the Data Science Stack
Independent and fast learner that can work alone and in teams
Ability to learn and explore new fields of expertise
Knowledge in some of the following can be an advantage: optimization, risk and finance.