AI Jobs

Benefits:

• Medical Insurance

• Competitive Incentive Program

Experience Requirements:

• Entry-level

Other Requirements:

• Be 18+ years old

• Legally eligible to work in Canada

• Fluent/Native in Lao/ Laotian

• Passing background checks

Responsibilities:

• Listening and transcribing audio or video files

• Data classification and data labeling

• Ability to meet deadlines and daily KPIs

• High degree of confidentiality required

Benefits:

• Medical Insurance

• Competitive Incentive Program

Experience Requirements:

• Entry-level

Other Requirements:

• Be 18+ years old

• Legally eligible to work in Canada

• Fluent/Native in Uzbek

• Passing background checks

Responsibilities:

• Listening and transcribing audio or video files

• Data classification and data labeling

• Ability to meet deadlines and daily KPIs

• Follow instructions, quality standards, and productivity requirements

Experience Requirements:

• 5 - 8 years

Experience Requirements:

• 5 - 9 years

Experience Requirements:

• 3 - 7 years

Education Requirements:

• Degree in software engineering, data science or similar area

Experience Requirements:

• 3+ years of experience as a Data Engineer

• Strong proficiency with SQL and relational databases

• Hands-on experience with Azure Data Factory and data lake architectures

• Experience with orchestration tools (e.g. Airflow, Prefect, Dagster)

• Solid programming skills in Python

Other Requirements:

• Experience with healthcare data (FHIR, HL7)

• Familiarity with HIPAA, GDPR, and ISO27001

• Willingness to travel for rare onsite sessions

Responsibilities:

• Build, maintain, and optimize batch and near-real-time data pipelines

• Design and evolve data models supporting AI/ML workflows

• Implement data quality checks, testing, and monitoring

• Implement API based data extraction and scraping technology

• Collaborate with AI/ML engineers and frontend teams

Experience Requirements:

• 2-5 Years

Other Requirements:

• Strong Python programming skills

• Experience with data engineering tools such as Pandas, PySpark, Airflow

• Hands-on experience with LLMs (e.g., OpenAI, Hugging Face)

• Proficiency in designing and deploying APIs using FastAPI or Flask

• Knowledge of SQL and relational databases like PostgreSQL

Responsibilities:

• Build and maintain scalable data preparation pipelines using Python

• Optimize data workflows to handle large datasets efficiently

• Develop, test, and refine prompts to optimize LLM outputs

• Design and deploy RESTful APIs to host LLM-based solutions

• Integrate APIs with monitoring and logging solutions

Education Requirements:

• Graduate degree in Computer Science, Statistics, Informatics, or Information Systems

Experience Requirements:

• 4-12 Years of experience required

• Five or more years of experience in a Data Engineer role

• Work experience in SAAS/B2C product development is a must

Other Requirements:

• Advanced SQL knowledge and experience with relational databases

• Knowledge of big data tools (Hadoop, Spark, Kafka, Dremio, Druid)

• Experience with AWS cloud services (EC2, EMR, RDS, Redshift, Snowflake)

• Familiarity with modern ETL/ELT tools like Airbyte and Hevodata

• Knowledge of Python and Java

Responsibilities:

• Create and maintain optimal data pipeline architecture

• Assemble large, complex data sets for business requirements

• Design scalable architecture addressing big data variety and velocity

• Build infrastructure for optimal ETL from wide variety of sources

• Implement database policies, procedures, and security standards

Education Requirements:

• Graduate or postgraduate degree in Computer Science, Statistics, Analytics, or Data Science

Experience Requirements:

• Role available at multiple levels (Jr, Sr, Principal)

• Experience deploying NLP solutions in a commercial environment

• Strong experience implementing models in production

Other Requirements:

• Deep understanding of machine learning and deep learning algorithms

• Expertise in developing NLP/NLU algorithms and models

• Programming skills in Python and R

• Knowledge of tools like TensorFlow, Keras, MXNET, and H2O

• Experience with chatbot technologies like Elasticsearch and Solr

Responsibilities:

• Research and develop algorithms for learning from text

• Extract data sets containing millions of documents

• Develop NLP capabilities to improve client service speed

• Summarize research and analysis for stakeholders

• Apply advancements in AI and deep tech to product needs

Education Requirements:

• Bachelor’s degree in Computer Science, Data Science, or a related field (or equivalent experience).

Experience Requirements:

• Strong programming skills with proficiency in Python and Node.js.

• Solid understanding of web scraping techniques and experience with relevant libraries and frameworks.

• Proficiency in SQL and experience working with relational databases for data storage and retrieval.

• Familiarity with version control systems, particularly Git.

• Knowledge of key-value stores, such as Redis.

Other Requirements:

• Experience with Elasticsearch or other search engines for data indexing and searching.

• Strong problem-solving skills and the ability to handle complex data extraction scenarios.

• Excellent attention to detail and the ability to ensure data accuracy and quality.

• Strong communication and collaboration skills to work effectively within a team environment.

• Familiarity with data privacy and security best practices.

Responsibilities:

• Develop and maintain web scraping scripts to extract data from a variety of sources.

• Collaborate with the data team to understand data requirements and implement effective scraping strategies.

• Conduct data quality assessments to ensure the accuracy and reliability of scraped data.

• Optimize scraping processes for efficiency and performance.

• Troubleshoot and resolve issues related to data extraction and scraping.

Education Requirements:

• Graduate degree in a quantitative field such as statistics, mathematics, data science, engineering, or computer science

Experience Requirements:

• 7+ years of experience as a Data Scientist or Machine Learning Solutions Architect

• 5+ years of proficiency in Python for data analysis, model training, evaluation, deployment, and optimization

• 1+ year of experience with technologies related to large language models

Other Requirements:

• Experience with open source frameworks like LangChain and LlamaIndex

• Knowledge of prompt engineering and state-of-the art model architectures

• Familiarity with AWS, Azure & Google services and the cloud computing landscape

• Knowledge of model deployment technologies such as TorchServe, ONNX, Triton, vLLM

• Familiarity with big data technologies such as Hadoop, Spark, and MongoDB

Responsibilities:

• Help lead the data science team fostering innovation and excellence

• Develop technical assets and IP across automated pentesting and real-time monitoring

• Engage with product owners to influence product direction

• Track and analyze emerging threats to AI systems

• Develop and implement detection and mitigation strategies for identified threats

Benefits:

• Health insurance (medical, dental, vision)

• Retirement planning (401k)

• Paid time off

Experience Requirements:

• 7+ years of experience building large-scale backend or distributed systems

• 2+ years managing or leading technical teams

• Deep experience with cloud-native distributed architectures (AWS preferred)

• Experience building or managing multi-tenant SaaS systems

• Experience operating systems in compliance-sensitive environments (healthcare, finance)

Other Requirements:

• Hands-on knowledge of event-driven systems (Kafka/Kinesis/PubSub)

• Proficient in modern programming languages (Python, Go, Java, or similar)

• Knowledge of distributed data storage (S3, RDS, DynamoDB, Delta Lake)

• Experience with workflow orchestration frameworks

• Flexible and have an entrepreneurial mindset

Responsibilities:

• Lead, mentor, and develop a team of backend and platform engineers

• Drive execution of critical platform initiatives like DICOM ingestion and data pipelines

• Provide hands-on technical guidance in system design and cloud-native architecture

• Work with Product Management to break down features into technical plans

• Partner with Data Science/ML teams to support model training and inference workflows

Benefits:

• equity

• benefits

Education Requirements:

• Ph.D. in Computational Biology, Bioinformatics, Statistics, Biophysics, or related field, or equivalent experience

Experience Requirements:

• Expertise in RNA biology and biochemistry, RNA-protein interactions, and RNA structure

• Proficiency in Python for data curation and analysis at scale

• Strong programming background, familiarity with Unix

• Strong foundation in statistics and experience with conducting statistical analysis of large-scale datasets

Other Requirements:

• Authorized to work lawfully in the US

• Open to working in-person at South San Francisco several days a week

Responsibilities:

• Provide RNA biology and RNA structure expertise on the ML team

• Enable and apply RNA-structure platform to prioritize RNA targets

• Generate insights from datasets on RNA structures and small molecule interactions

• Curate RNA datasets for training of ML models

• Inform scientific questions and ML model development

Education Requirements:

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or Statistics

Experience Requirements:

• 7+ years of hands-on experience in applied machine learning and data science

• 2+ years in a leadership or managerial role

• Success in adapting foundation models through fine-tuning or transfer learning

Other Requirements:

• Expertise with PEFT approaches (LoRA, prefix tuning, etc.)

• Proficiency in PyTorch, TensorFlow, and Hugging Face

• Knowledge of GPU/TPU optimization and scaling ML workloads

Responsibilities:

• Lead and mentor a team of applied scientists and ML engineers

• Translate cutting-edge research into practical, scalable business solutions

• Design, evaluate, and improve models using robust validation strategies

• Collaborate with product, engineering, and business stakeholders

• Articulate complex ML concepts to technical and non-technical audiences

Education Requirements:

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or Statistics

Experience Requirements:

• 3+ years of hands-on experience in applied machine learning and data science

• Experience training and fine-tuning large-scale models (LLMs, transformers)

• Experience working on applied AI problems across NLP or computer vision

Other Requirements:

• Hands-on expertise with PEFT approaches such as LoRA

• Proficiency in PyTorch, TensorFlow, and Hugging Face

• Knowledge of GPU/TPU optimization and mixed precision training

Responsibilities:

• Design, evaluate, and improve models using validation strategies

• Adapt foundation models to domain-specific applications

• Collaborate with product and engineering stakeholders

• Translate research into practical, scalable solutions

• Provide technical guidance to junior scientists

Education Requirements:

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or Statistics

Experience Requirements:

• 5+ years of hands-on experience in applied machine learning and data science

• Experience in training and fine-tuning large-scale models

• Proven record of adapting foundation models through transfer learning

Other Requirements:

• Expertise in PEFT approaches and quantization-aware training

• Proficiency in Spark, Databricks, or cloud-native ML services

• Excellent communication and presentation skills

Responsibilities:

• Lead and mentor a team of applied scientists and ML engineers

• Design, evaluate, and improve models using bias/fairness checks

• Work with product and engineering teams to deliver AI solutions

• Translate research breakthroughs into scalable production systems

• Ensure performance optimization of AI models

Benefits:

• Comprehensive medical, vision, and dental coverage

• 401(k) retirement plan

• Short & long-term disability insurance

• Life insurance

• Paid parental leave

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field

Experience Requirements:

• 5+ years of experience in backend software engineering

• 2+ years leading AI/ML teams

• Hands-on experience training AI/ML models

• Proven experience building and deploying agentic applications

Other Requirements:

• Must be located in the Toronto area

• Fluency in Python, Java, Scala, or C++

• Knowledge of ML toolkits (PyTorch, TensorFlow, LangChain, etc.)

Responsibilities:

• Partner directly with cofounders to set AI engineering strategy

• Hire, lead, and mentor a founding AI engineering team

• Act as a player-coach: contribute to architecture and model training

• Design and build scalable ML infrastructure for production

• Lead the development and launch of agentic applications

Education Requirements:

• Bachelor’s, Master's degree or PhD in Technology or related technical field

• Bachelor’s degree in Information Systems, Business, or a related field

Experience Requirements:

• 4+ years of experience working with ERP systems, with strong hands-on SAP experience

• Deep hands-on experience with SAP ECC and/or S/4HANA

• Experience supporting or mapping data across systems such as Oracle, Microsoft Dynamics, NetSuite

• Strong SQL and data modeling experience

• Comfort working with data engineers, writing specifications, and validating outputs

Other Requirements:

• Candidates must be authorized to work in the U.S. without sponsorship

• Strong communication skills and ability to work cross-functionally

• Experience with AI platforms, Large Language Models, Generative AI, Cloud AI

• An understanding of cloud services: compute, storage, accelerated computing

• Knowledge of master data domains (Customer, Vendor, Material, Finance)

Responsibilities:

• Act as Keystone.ai’s expert on SAP and ERP data structures

• Define mapping logic between raw ERP tables and our scientific data model

• Partner with product and data engineers to build ingestion templates and transformation logic

• Create schema reference guides, field usage dictionaries, and normalization logic

• Ensure that our platform supports customer-specific schema variants

Experience Requirements:

• Minimum of eight (8) years of combined experience in healthcare (claims processing, fraud detection, etc.).

• Minimum of eight (8) years experience auditing medical claims to identify improper payments.

• Advanced proficiency with Excel and comfort working with large data sets.

• Working knowledge of medical billing codes including CPT, ICD-10-PCS, ICD-10-CM, HCPCS, and NDC.

• Experience in various claim payment methodologies for professional, facility, and ancillary providers.

Other Requirements:

• Applicants must already be legally authorized to work in the U.S.

• Ability to maintain confidentiality and comply with HIPAA and data security standards.

Responsibilities:

• Review, prioritize, and independently work assigned Data Mining leads to determine verification steps.

• Investigate and validate payment terms using internal systems, payer portals, and contracts.

• Reconcile discrepancies across sources and drive cases to a clear, audit-ready determination.

• Serve as a Data Mining Subject Matter Expert, providing mentoring and coaching to specialists.

• Identify and solve problems by surfacing errors and overpayments and documenting root causes.

Experience Requirements:

• Minimum of one (1) year of combined experience in healthcare (health insurance, claims processing, etc.).

• Minimum of one (1) year experience auditing medical claims to identify improper payments.

• Minimum of one (1) year of experience performing data analytics with large data sets.

• Minimum of one (1) year of experience in medical billing codes (CPT, ICD-10, HCPCS, etc.).

Other Requirements:

• Applicants must already be legally authorized to work in the U.S.

• Ability to maintain confidentiality and comply with HIPAA and data security standards.

Responsibilities:

• Review and work assigned Data Mining leads to determine verification steps and next actions.

• Investigate and validate payment terms using internal systems and payer portals.

• Create clear, detailed, and accurate case notes capturing verification steps and outcomes.

• Track outcomes and error categories to identify root causes of recurring issues.

• Reconcile discrepancies across eligibility feeds, member data, and claim history.