About Inizio Ignite

We are Inizio Ignite, the global advisory partner for health and life sciences encompassing connected expertise across Research Partnership, Putnam, STEM, and Vynamic. Guided by purpose, passion, and precision, we collaborate with clients to ignite impact, accelerate performance, and deliver lasting change for patients. By uniting strategy, insights, and innovation with a unique depth and breadth of expertise, we drive transformation and shape the future of health. Proudly part of Inizio – the market-leading commercialization partner for health and life sciences. From early-stage development to loss of exclusivity, we simplify complexity, unlock value, and make collaboration seamless at every pivotal moment – helping healthcare leaders shape strategy and drive meaningful change. 

About the Role

We are seeking an experienced Data Science Manager to join our team at Inizio Ignite. The ideal candidate will bring deep machine learning expertise, strong GenAI and cloud competencies, and hands-on experience delivering analytical solutions that drive meaningful business impact for life sciences clients.

This leader will manage data scientists, oversee project delivery, partner with cross-functional teams, and shape the development of innovative AI/ML solutions.

What you'll be doing

Leadership & Delivery

• Lead and manage a team of data scientists, providing technical guidance, mentorship, and performance oversight.
• Own end-to-end delivery of data science and GenAI engagements for pharma/biotech clients.
• Translate business problems into analytical frameworks and ensure high-quality, timely project execution.
• Drive adoption of AI/ML capabilities across client engagements and internal initiatives.

Technical Responsibilities

• Architect, design, and review ML, statistical, and GenAI solutions, ensuring scalability, accuracy, and alignment with business objectives.
• Oversee the development, validation, and deployment of predictive models, NLP pipelines, and GenAI-based applications (e.g., summarization, insights automation, generative workflows).
• Lead cloud-based solution deployment (AWS, Azure, or GCP) including orchestration, MLOps best practices, and integration with enterprise systems.
• Ensure adherence to model governance, compliance, and responsible AI frameworks.

What you'll need

• Extensive experience in data science or advanced analytics roles, with at least 2–3 years in a leadership or managerial capacity.
• Ideally prior experience in pharmaceutical, biotech, or healthcare consulting is mandatory.
• Strong expertise in machine learning, NLP, deep learning, and GenAI (LLMs, prompt engineering, RAG, fine-tuning, embeddings).
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP; exposure to MLOps frameworks preferred.
• Proficiency in Python, SQL, and common DS/ML libraries (TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain, etc.).
• Proven experience managing teams, delivering client projects, and presenting to senior stakeholders.
• Good to have understanding of pharma datasets (claims, RWE, clinical, medical insights, social data) and key use cases.
• Experience building GenAI solutions in production environments

Our Pledge

At Inizio, we value inclusivity, recognize the power of diversity, and inspire the next generation of change-makers. We are an equal opportunities employer and are committed to creating a dynamic work environment that values diversity, equity, and inclusion. We welcome all applications regardless of race, color, religion, gender identity, age, national origin, marital status, veteran status, genetic information, sexual orientation, or disability.

Artificial Intelligence

Inizio may use AI technologies to support certain aspects of our recruitment process, but all hiring decisions are made by our human recruiting team. We are committed to fair, inclusive, and human-led hiring practices. Don't meet every requirement? That's okay! We are dedicated to building a diverse, inclusive, and authentic workplace. If you're excited about this role but your experience doesn't perfectly align with every qualification, we encourage you to apply anyway.

Job Title: Senior Full Stack Machine Learning Engineer

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are looking for a talented and experienced Senior Full Stack Machine Learning Engineer to join our dynamic team.

Job Description:

• As a Senior Full Stack Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying machine learning models that drive our healthcare solutions. You will work closely with data scientists, software engineers, and product managers to build scalable and robust machine learning systems. Your expertise will help us transform healthcare data into actionable insights, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Model Development: Design, implement, and optimize machine learning models for various healthcare applications, including predictive analytics, natural language processing, and generative AI.
• End-to-End Deployment: Develop and maintain the full lifecycle of machine learning solutions, from data preprocessing and model training to deployment and monitoring in production environments.
• Data Engineering: Collaborate with data engineers to build and maintain data pipelines, ensuring the availability of high-quality data for training and inference.
• Software Development: Write clean, efficient, and maintainable code for machine learning applications, ensuring seamless integration with existing systems.
• Performance Optimization: Continuously monitor and improve the performance of machine learning models and systems, addressing issues related to scalability, latency, and accuracy.
• Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
• Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of continuous learning and innovation.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in machine learning, with a proven track record of deploying models in production environments.

Technical Skills:

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in building applications leveraging generative AI technologies which includes LLM’s, prompt engineering, Vector Databases, RAG architectures and transfer learning is a plus.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Strong knowledge of data structures, algorithms, and software engineering best practices.
• Familiarity with big data technologies (e.g., Hadoop, Spark) and data pipeline tools (e.g., Airflow, Kafka).
• Experience with frontend and backend development, including frameworks such as React, Node.js, and Django.

Domain Knowledge: Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.

Soft Skills:

• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.
• Ability to work independently and as part of a team in a fast-paced environment.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful projects that improve healthcare outcomes.
• Collaborative and innovative work environment.
• Professional development and growth opportunities.
• Flexible work arrangements, including hybrid and remote options.

Join us in our mission to transform healthcare through technology! We look forward to hearing from you.

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes.

Role overview

 As an MLOps Engineer, you will design, build, and operate the infrastructure and tooling that power end-to-end ML workflows on AWS, including SageMaker, Bedrock, and Snowflake Cortex. You will partner closely with data scientists, ML engineers, and platform teams to ensure models are reliable, secure, and scalable in production.

Key responsibilities

• Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code.
• Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring.
• Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake.
• Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services.
• Automate environment provisioning, configuration, and dependency management across dev, test, and production.
• Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA).
• Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services.
• Contribute to best practices, standards, and documentation for ML platform and operations across the organization.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
• 4+ years of experience in software engineering, data engineering, or ML engineering with at least 2+ years focused on MLOps or ML platform work.
• Strong proficiency with Python and experience integrating ML libraries or frameworks (e.g., scikit-learn, TensorFlow, PyTorch) into production workflows.
• Hands-on expertise with AWS services relevant to MLOps: SageMaker, Bedrock, IAM, CloudWatch, ECR, ECS/EKS or Lambda, S3, Step Functions, and Glue.
• Experience with Snowflake (including Snowflake Cortex), SQL, and building secure, performant data pipelines into and out of Snowflake.
• Proficiency with CI/CD tools (e.g., GitHub Actions, GitLab CI, CodePipeline) and infrastructure-as-code (e.g., Terraform, CloudFormation, CDK).
• Familiarity with containerization and orchestration (Docker, Kubernetes) and event streaming tools (e.g., Kafka) is a plus.
• Knowledge of software engineering best practices, including testing, code reviews, version control, and design for reliability and scalability.
• Experience in regulated domains or with healthcare data standards and regulations is a plus (e.g., HIPAA, FHIR, HL7).

Soft skills and benefits

• Excellent problem-solving and analytical skills with a focus on reliability and automation.
• Strong communication and collaboration abilities, including working cross-functionally with engineering, data science, and product teams.
• Ability to work independently in a fast-paced environment
• Competitive salary and benefits package.
• Opportunity to work on impactful ML platforms that improve healthcare outcomes.
• Collaborative, innovative environment with professional development and growth opportunities.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (10+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are seeking a highly skilled Senior Machine Learning SDET to ensure the quality, reliability, and safety of our ML-powered healthcare products.

Job Description:

As a Senior Machine Learning SDET, you will be responsible for designing, implementing, and maintaining robust testing frameworks and quality strategies for machine learning systems across their entire lifecycle. You will collaborate closely with data scientists, ML engineers, software engineers, and product managers to validate models, data pipelines, and ML-driven services in production environments. Your work will help ensure that our ML solutions are accurate, performant, secure, and compliant with healthcare standards, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Test Strategy for ML Systems: Define and own the end-to-end test strategy for ML models, data pipelines, and services, including functional, performance, regression, and reliability testing.
• ML Model Validation: Design automated tests to validate model behavior (e.g., accuracy, drift detection, bias, stability, and robustness) across training, staging, and production environments.
• Data Quality & Pipeline Testing: Build automated tests and monitoring for data pipelines to ensure schema integrity, data completeness, data freshness, and correctness for training and inference.
• Test Automation Frameworks: Develop and maintain scalable test automation frameworks and tools for APIs, microservices, and ML workflows, integrating them into CI/CD pipelines.
• Performance & Scalability: Design and execute load and performance tests for ML inference services and batch jobs, focusing on latency, throughput, and resource utilization.
• Monitoring & Observability: Collaborate with ML and platform engineers to implement monitoring, logging, and alerting for ML systems, including model performance and data drift.
• Shift-Left Quality: Embed quality practices early in the ML development lifecycle, including testable model design, test data generation, and automated validation in CI.
• Tooling & Infrastructure: Leverage and extend existing tools (e.g., Python test frameworks, cloud services, containerization, big data tools) to support robust ML testing.
• Collaboration: Work closely with cross-functional teams to understand business and regulatory requirements and translate them into verifiable test scenarios and acceptance criteria.
• Mentorship: Provide guidance and mentorship to engineers on best practices for testing ML and data-intensive systems, fostering a culture of quality and continuous improvement.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field; Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in software test engineering or SDET roles, with at least 3 years focused on testing machine learning, data, or distributed backend systems in production.

Technical Skills:

• Strong proficiency in Python and testing frameworks such as pytest, unittest, or similar, with experience testing ML code and data pipelines.
• Hands-on experience with ML libraries and ecosystems (e.g., TensorFlow, PyTorch, scikit-learn) sufficient to design model validation and testing strategies.
• Hands-on experience with generative AI concepts (LLMs, RAG, vector databases, prompt engineering, and transfer learning) and their testing implications is a strong plus.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes) for deploying and testing ML services.
• Strong knowledge of data structures, algorithms, and software engineering best practices, including code reviews, version control, and CI/CD.
• Experience building and integrating automated tests for REST/gRPC APIs and microservices, including contract and integration testing.
• Experience with performance and load testing tools and techniques for backend and ML inference services.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, ELK, or cloud-native equivalents) for tracking model and system health.

Domain Knowledge:

• Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.
• Experience testing systems in regulated, high-compliance, or safety-critical environments is highly desirable.

Soft Skills:

• Excellent problem-solving and analytical skills, with a strong focus on edge cases, failure modes, and risk assessment.
• Strong communication and collaboration abilities, with comfort working across ML, data, product, and platform teams.
• Ability to work independently and as part of a team in a fast-paced environment, balancing quality, speed, and pragmatism.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful ML-driven healthcare products that improve patient outcomes and operational efficiency.
• Collaborative and innovative work environment.
• Professional development and growth opportunities in ML quality engineering and SDET leadership.
• Flexible work arrangements, including hybrid and remote options.

Join us in ensuring that cutting-edge AI and machine learning solutions are not only powerful but also safe, reliable, and trustworthy in healthcare. We look forward to hearing from you.

The Role

We’re looking for a seasoned Staff Machine Learning Engineer to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale satellite and aerial imagery into actionable insights. You’ll lead end-to-end ownership—from model deployment and MLOps to infrastructure design—while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions. If you thrive at the intersection of deep technical expertise, system design, and cross-functional collaboration, this role is for you.

How you'll make an impact:

ML System Architecture & Production Deployment

• Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery
• Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock,or custom-built solutions
• Implement MLflow for experiment tracking, model versioning, and model registry management
• Architect batch inference systems optimized for throughput and cost-efficiency
• Work with geospatial data formats and coordinate reference systems
• Collaborate with data scientists to transition models from research to production
• Partner with platform engineering to build scalable compute, GPU clusters, and storage layers

ML Operations & Reliability

• Implement comprehensive model monitoring systems to track performance degradation and data drift
• Design and execute canary deployments and A/B testing frameworks for safe model rollouts
• Build active learning pipelines to continuously improve model performance with minimal labeling effort
• Establish model evaluation frameworks and performance benchmarking processesCreate alerting and observability systems for production ML workloads

Technical Leadership

• Mentor ML engineers and data scientists on best practices for production ML
• Drive technical decision-making on ML infrastructure and tooling
• Collaborate with platform and data engineering teams to optimize the ML stack
• Establish engineering standards and contribute to architectural roadmaps

What we’re looking for:

• 5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
• Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker,Lambda, ECS/EKS, S3, etc.)
• Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management
• Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow
• Production experience with ensemble models (xgboost, lightgbm, RF)

ML Operations Expertise

• Experience implementing model monitoring, drift detection, and alerting systems
• Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models
• Knowledge of active learning strategies and human-in-the-loop ML systems
• Strong understanding of model evaluation metrics, bias detection, and performance analysis

Technical Skills

• Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc)
• Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark,Dask, Ray or similar)
• Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation)
• Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm
• Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatterns
• Experience with containerization (Docker, Kubernetes) for model deployment
• Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc
• Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling

We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.