AI Jobs

Education Requirements:

• Currently pursuing a degree in Sports Management, Statistics, Mathematics, Data Science, Economics, or a related field. Recent graduates may also be considered

Experience Requirements:

• Strong proficiency in data analysis tools such as Excel, R, Python, and SQL. Familiarity with sports analytics platforms is a plus

• Basic understanding of statistical techniques (regression analysis, correlation, hypothesis testing, etc.)

• Passion for sports and understanding of various sports

• Excellent communication skills, both written and verbal, with the ability to convey complex data findings in an easy-to-understand format

• Strong attention to detail and problem-solving skills

Responsibilities:

• Assist in gathering and organizing data from various sources, including game statistics, player performance data, and historical records

• Maintain and update databases to ensure accuracy and reliability of information

• Help with cleaning and preprocessing raw data to make it suitable for analysis

• Analyze sports data using statistical methods and software (e.g., Excel, R, Python, SQL, etc.) to generate insights about player performance, team dynamics, and other relevant metrics

• Support the creation of visualizations (charts, graphs, dashboards) to present data findings in a clear and understandable way

Experience Requirements:

• 8+ years of experience in data analysis

Other Requirements:

• Proactive, entrepreneurial, and collaborative

• Strong familiarity with tools like Adobe Analytics/Omniture, Mixpanel, Heap, Amplitude, Google Analytics 360, GA4, Google Search Console, Billing systems, etc.

• Experience with database analysis (SQL, R, Python, BigQuery, Snowflake, Airflow, etc.)

• Clear understanding of statistics, predictive analytics, and machine learning

• Experience in business analytics

Responsibilities:

• Work with clients to understand pain points and translate them into requirements for queries and models

• Design tables and views providing ongoing data in useful formats

• Identify key insights not being tracked and predicted in software

• Assist Dev and QA in ensuring accuracy and understandability of client deliverables

• Collaborate with design and development team to make adjustments to models, presentation layers, and queries

Education Requirements:

• PhD or Master’s degree in Computer Science or equivalent industry experience

Experience Requirements:

• 2+ years of experience in AI research, including training, fine-tuning, and experimenting with foundation models beyond black-box use

• Track record of first-author publications at top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, ACL, EMNLP)

• Strong proficiency in one or more deep learning frameworks (e.g., JAX, PyTorch)

• Experience communicating complex research to peers

• Solid knowledge of programming languages and experience in developing, debugging, and optimizing beyond ML systems

Other Requirements:

• Experience in low-level machine learning optimization, such as writing CUDA kernel code

Education Requirements:

• Doctoral

Benefits:

• Generously matched contributions

• Vitality Health (London employees)

• Life insurance (London employees)

• 25 days holiday plus bank holidays

• Learning & development fund

Other Requirements:

• Bilingual - Hindi/English

Benefits:

• Medical, Dental & Vision coverage

• 401(k) Plan

• Unlimited PTO

• Two annual company-wide, week-long breaks

• Flexible work environment

Benefits:

• Medical, Dental & Vision coverage

• 401(k) Plan

• Unlimited PTO

• Two annual company-wide, week-long breaks

• Flexible work environment

Benefits:

• Medical, Dental & Vision coverage

• 401(k) Plan

• Unlimited PTO

• Two annual company-wide, week-long breaks

• Flexible work environment

Benefits:

• Medical, Dental & Vision coverage

• 401(k) Plan

• Unlimited PTO

• Two annual company-wide, week-long breaks

• Flexible work environment

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field. Ph.D. preferred

Experience Requirements:

• 5+ years of industry experience in machine learning engineering or related roles

Other Requirements:

• Expertise in machine learning techniques

• Proficiency in programming languages such as Python

• Strong understanding of cloud computing platforms

• Hands-on experience with containerization technologies

• Solid understanding of software engineering best practices

• Excellent problem-solving skills

• Strong communication and collaboration skills

Responsibilities:

• Design, develop, and deploy state-of-the-art machine learning algorithms and models

• Collaborate with cross-functional teams

• Lead end-to-end machine learning projects

• Architect scalable and efficient ML pipelines

• Optimize model performance and scalability

Education Requirements:

• Currently Enrolled or graduated with MS or PhD in Computer Science, Electrical Engineering, Statistics, or equivalent fields. Specialization in machine learning preferred

Experience Requirements:

• Strong understanding of machine learning algorithms & principles (regression analysis, time series, probabilistic models, supervised classification and unsupervised learning), and their application

• Experience with Deep Learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks and LSTM

• Familiarity with Deep Learning frameworks such as TensorFlow and PyTorch, and strong experience in at least one of those

• Excellent programming skills in prototyping languages such as Python and R. Python is preferred

Responsibilities:

• participating in developing and deploying fault-tolerant ETL pipelines, perform data visualization and enable the development of ML algorithms

Benefits:

• Competitive salary and equity

• Medical, dental, vision benefits

• Learning budget per year

• Unlimited PTO policy with minimum time off requirements

• Flexible working hours on many teams

Education Requirements:

• PhD

• Master's degree and 3+ years of experience

Experience Requirements:

• Significant hands-on experience in AI/ML, specifically with large datasets

• Strong coding skills with experience in cloud computing

• Good communication skills

Other Requirements:

• Domain expertise in climate is a plus

• Strong communication skills

Responsibilities:

• Building applications of GenAI approaches to solve forecasting and interpretation challenges in climate science

• Improving food and climate security outcomes

• Working cross-functionally with climate scientists, ML engineers, software engineers, and business teams

Experience Requirements:

• detailed knowledge of deep learning for computer vision

• knowledge of deep learning infrastructure and efficient computing

Responsibilities:

• build the future of creative tooling

Benefits:

• Employee Stock Options

• Extensive Health Insurance Coverage

• Team Outings

• Delicious Meals and Snacks at the Office

• Generous Leave and Paid Time Off Policies

Education Requirements:

• Graduate degree in computer science or related field

Experience Requirements:

• Strong knowledge of theoretical and practical aspects of machine learning, including probability, statistics, learning theory, algorithms, and software architecture.

• Proven ability to deliver machine learning-driven solutions, particularly in production environments.

• Experience in Python programming for machine learning projects, with a solid understanding of CI/CD pipelines and deploying models using AWS or other cloud platforms.

• Good understanding of applied machine learning concepts, with experience in deploying ML models as microservices; previous experience with Conversational AI or NLP projects is a plus

Other Requirements:

• 2+ years of industrial experience

• Atleast 1 year of experience in Data Science or Machine learning (NLP/Speech would be preferable)

Responsibilities:

• Building ML models on our core ML platform; and monitor, diagnose and maintain active deployments.

• Create and own technical design for ML models associated with the voice-bot.

• Own the ML metrics related to bots - Intent, SCR, resolution, etc. and responsible for continuous improvement of ML performance

• Define and drive quality, performance, robustness, and reliability of our deliveries by collaborating with delivery stakeholders.

• Drive and organize research, discussion and reviews towards continuous improvements of our models and capabilities

Benefits:

• Employee Stock Options

• Extensive Health Insurance Coverage

• Team Outings

• Delicious Meals and Snacks at the Office

• Generous Leave and Paid Time Off Policies

Experience Requirements:

• Strong knowledge of SQL

• Working knowledge of scripting (one of Python, R)

• Experience working with at least 1 BI Tool (Superset, Tableau, Metabase, Google Analytics, Clevertap, MixPanel etc.)

• Strong communication skills - ability to effectively communicate the insights developed to the various stakeholders in the organization

• Able to work with agile product development teams and able to identify new ways to help your team be effective

Other Requirements:

• Brownie points to folks who’ve built or worked on analyzing conversational AI products

• Experience working on Machine Learning and NLP systems

Responsibilities:

• Support the product and delivery teams to drive improved product quality and customer satisfaction by leveraging data to study business and UX impact for features and product improvements

• Deep dive into data and identify right problem statements and right segments to target and take experimentation driven product delivery approaches.

• Work closely with Tech, Product, CS and Delivery to deliver standard and custom dashboards to enable understanding of bot health and performance with minimal guidance

• Capture insights from customer feedback, experiments, research, and data analysis to identify trends and pinpoint the drivers behind them; translate these insights into tactical solutions to improve our voicebot deliveries

• Becoming the domain champion of the product/function with a clear data-driven understanding of how callers interact with our voicebot

Benefits:

• A chance to grow within a leading, fast-moving AI company

• A competitive compensation package with equity shares

• A diverse and friendly environment

• Social and company events

• Startup offices in central Oslo

Education Requirements:

• Master's degree or higher in Electrical Engineering, Computer Science, or a related field

Experience Requirements:

• Hands-on experience with (audio) signal processing

• 2+ years of industry experience in applying machine learning to real-world problems

• Experience using development using software engineering best practices, including code reviews, continuous integration, and tooling for development environments

• MLOps experience. E.g., scaling training in cloud environments, model versioning, and model performance profiling

Other Requirements:

• Excellent problem-solving and communication skills

Responsibilities:

• Develop and optimize machine learning algorithms for real-time audio enhancement

• Research improvements in machine learning models for audio

• Build, administer, and scale ML processing pipelines

• We believe in end-to-end ownership; this role will involve taking ML models from development to production at scale

Benefits:

• Reasonable expenses may be reimbursed

Responsibilities:

• work on our deep generative models, e.g., GANs, for asset generation

• work on the latest domain adaptation techniques alongside our largest customers

Education Requirements:

• BSc./MSc. In CS or similar – an advantage

Experience Requirements:

• 5+ years experience as a Machine Learning Engineer

• 10+ years of experience with Python/Java/Scala

• Strong understanding of distributed systems, object-oriented programming and design patteri

• Distributed Compute frameworks such as Spark, Dask, Ray etc

• Hands-on experience designing, training, and deploying machine-learning models

Responsibilities:

• Work on pecans’s core data and machine learning infrastructure

• Create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation