Senior ML Engineer Job at The Muse (Bangalore)

Senior Software Engineer - ML Infrastructure

We build simple yet innovative consumer products and developer APIs that shape h...

Location

United States , San Francisco

Salary:

180000.00 - 270000.00 USD / Year

Plaid

Expiration Date

Until further notice

Requirements

5+ years of industry experience as a software engineer, with strong focus on ML/AI infrastructure or large-scale distributed systems
Hands-on expertise in building and operating ML platforms (e.g., feature stores, data pipelines, training/inference frameworks)
Proven experience delivering reliable and scalable infrastructure in production
Solid understanding of ML Ops concepts and tooling, as well as best practices for observability, security, and reliability
Strong communication skills and ability to collaborate across teams

Job Responsibility

Design and implement large-scale ML infrastructure, including feature stores, pipelines, deployment tooling, and inference systems
Drive the rollout of Plaid’s next-generation feature store to improve reliability and velocity of model development
Help define and evangelize an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring
Ensure operational excellence of ML pipelines and services, including reliability, scalability, performance, and cost efficiency
Collaborate with ML product teams to understand requirements and deliver solutions that accelerate experimentation and iteration
Contribute to technical strategy and architecture discussions within the team
Mentor and support other engineers through code reviews, design discussions, and technical guidance

What we offer

medical, dental, vision, and 401(k)

Fulltime

Senior ML Data Engineer

As a Senior Data Engineer, you will play a pivotal role in our AI/ML workstream,...

Location

Salary:

Not provided

Awin Global

Expiration Date

Until further notice

Requirements

Bachelor or Master’s degree in data science, data engineering, Computer Science with focus on math and statistics / Master’s degree is preferred
At least 5 years experience as AI/ML data engineer undertaking above task and accountabilities
Strong foundation in computer science principes and statistical methods
Strong experience with cloud technology (AWS or Azure)
Strong experience with creation of data ingestion pipeline and ET process
Strong knowledge of big data tool such as Spark, Databricks and Python
Strong understanding of common machine learning techniques and frameworks (e.g. mlflow)
Strong knowledge of Natural language processing (NPL) concepts
Strong knowledge of scrum practices and agile mindset

Job Responsibility

Design and maintain scalable data pipelines and storage systems for both agentic and traditional ML workloads
Productionise LLM- and agent-based workflows, ensuring reliability, observability, and performance
Build and maintain feature stores, vector/embedding stores, and core data assets for ML
Develop and manage end-to-end traditional ML pipelines: data prep, training, validation, deployment, and monitoring
Implement data quality checks, drift detection, and automated retraining processes
Optimise cost, latency, and performance across all AI/ML infrastructure
Collaborate with data scientists and engineers to deliver production-ready ML and AI systems
Ensure AI/ML systems meet governance, security, and compliance requirements
Mentor teams and drive innovation across both agentic and classical ML engineering practices
Participate in team meetings and contribute to project planning and strategy discussions

What we offer

Flexi-Week and Work-Life Balance: We prioritise your mental health and well-being, offering you a flexible four-day Flexi-Week at full pay and with no reduction to your annual holiday allowance. We also offer a variety of different paid special leaves as well as volunteer days
Remote Working Allowance: You will receive a monthly allowance to cover part of your running costs. In addition, we will support you in setting up your remote workspace appropriately
Pension: Awin offers access to an additional pension insurance to all employees in Germany
Flexi-Office: We offer an international culture and flexibility through our Flexi-Office and hybrid/remote work possibilities to work across Awin regions
Development: We’ve built our extensive training suite Awin Academy to cover a wide range of skills that nurture you professionally and personally, with trainings conveniently packaged together to support your overall development
Appreciation: Thank and reward colleagues by sending them a voucher through our peer-to-peer program

Senior ML Engineer

As a Senior ML Engineer at Provectus, you'll be responsible for designing, devel...

Location

Colombia , Medellín; Bogotá; Cali; Barranquilla; Bucaramanga

Salary:

Not provided

Provectus

Expiration Date

Until further notice

Requirements

ML Fundamentals: supervised, unsupervised, and reinforcement learning
Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation
ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks
Deep Learning: CNNs, RNNs, Transformers
LLM Applications: Experience building production LLM-based applications
Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies
RAG Systems: Experience building retrieval-augmented generation architectures
Vector Databases: Familiarity with embedding models and vector search
LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs
Python: Advanced proficiency in Python for ML applications

Job Responsibility

Design and implement end-to-end ML solutions from experimentation to production
Build scalable ML pipelines and infrastructure
Optimize model performance, efficiency, and reliability
Write clean, maintainable, production-quality code
Conduct rigorous experimentation and model evaluation
Troubleshoot and resolve complex technical challenges
Mentor junior and mid-level ML engineers
Conduct code reviews and provide constructive feedback
Share knowledge through documentation, presentations, and workshops
Collaborate with cross-functional teams (DevOps, Data Engineering, SAs)

Senior ML Data Engineer

As a Senior Data Engineer, you will play a pivotal role in our AI/ML workstream,...

Location

Poland , Warsaw

Salary:

Not provided

Awin Global

Expiration Date

Until further notice

Requirements

Bachelor or Master’s degree in data science, data engineering, Computer Science with focus on math and statistics / Master’s degree is preferred
At least 5 years experience as AI/ML data engineer undertaking above task and accountabilities
Strong foundation in computer science principes and statistical methods
Strong experience with cloud technology (AWS or Azure)
Strong experience with creation of data ingestion pipeline and ET process
Strong knowledge of big data tool such as Spark, Databricks and Python
Strong understanding of common machine learning techniques and frameworks (e.g. mlflow)
Strong knowledge of Natural language processing (NPL) concepts
Strong knowledge of scrum practices and agile mindset
Strong Analytical and Problem-Solving Skills with attention to data quality and accuracy

Job Responsibility

Design and maintain scalable data pipelines and storage systems for both agentic and traditional ML workloads
Productionise LLM- and agent-based workflows, ensuring reliability, observability, and performance
Build and maintain feature stores, vector/embedding stores, and core data assets for ML
Develop and manage end-to-end traditional ML pipelines: data prep, training, validation, deployment, and monitoring
Implement data quality checks, drift detection, and automated retraining processes
Optimise cost, latency, and performance across all AI/ML infrastructure
Collaborate with data scientists and engineers to deliver production-ready ML and AI systems
Ensure AI/ML systems meet governance, security, and compliance requirements
Mentor teams and drive innovation across both agentic and classical ML engineering practices
Participate in team meetings and contribute to project planning and strategy discussions

What we offer

Flexi-Week and Work-Life Balance: We prioritise your mental health and well-being, offering you a flexible four-day Flexi-Week at full pay and with no reduction to your annual holiday allowance. We also offer a variety of different paid special leaves as well as volunteer days
Remote Working Allowance: You will receive a monthly allowance to cover part of your running costs. In addition, we will support you in setting up your remote workspace appropriately
Pension: Awin offers access to an additional pension insurance to all employees in Germany
Flexi-Office: We offer an international culture and flexibility through our Flexi-Office and hybrid/remote work possibilities to work across Awin regions
Development: We’ve built our extensive training suite Awin Academy to cover a wide range of skills that nurture you professionally and personally, with trainings conveniently packaged together to support your overall development
Appreciation: Thank and reward colleagues by sending them a voucher through our peer-to-peer program

Senior Platform Engineer, ML Data Systems

We’re looking for an ML Data Engineer to evolve our eval dataset tools to meet t...

Location

United States , Mountain View

Salary:

137871.00 - 172339.00 USD / Year

Khan Academy

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
5 years of Software Engineering experience with 3+ of those years working with large ML datasets, especially those in open-source repositories such as Hugging Face
Strong programming skills in Go, Python, SQL, and at least one data pipeline framework (e.g., Airflow, Dagster, Prefect)
Experience with data versioning tools (e.g., DVC, LakeFS) and cloud storage systems
Familiarity with machine learning workflows — from training data preparation to evaluation
Familiarity with the architecture and operation of large language models, and a nuanced understanding of their capabilities and limitations
Attention to detail and an obsession with data quality and reproducibility
Motivated by the Khan Academy mission “to provide a free world-class education for anyone, anywhere.”
Proven cross-cultural competency skills demonstrating self-awareness, awareness of other, and the ability to adopt inclusive perspectives, attitudes, and behaviors to drive inclusion and belonging throughout the organization.

Job Responsibility

Evolve and maintain pipelines for transforming raw trace data into ML-ready datasets
Clean, normalize, and enrich data while preserving semantic meaning and consistency
Prepare and format datasets for human labeling, and integrate results into ML datasets
Develop and maintain scalable ETL pipelines using Airflow, DBT, Go, and Python running on GCP
Implement automated tests and validation to detect data drift or labeling inconsistencies
Collaborate with AI engineers, platform developers, and product teams to define data strategies in support of continuously improving the quality of Khan’s AI-based tutoring
Contribute to shared tools and documentation for dataset management and AI evaluation
Inform our data governance strategies for proper data retention, PII controls/scrubbing, and isolation of particularly sensitive data such as offensive test imagery.

What we offer

Competitive salaries
Ample paid time off as needed
8 pre-scheduled Wellness Days in 2026 occurring on a Monday or a Friday for a 3-day weekend boost
Remote-first culture - that caters to your time zone, with open flexibility as needed, at times
Generous parental leave
An exceptional team that trusts you and gives you the freedom to do your best
The chance to put your talents towards a deeply meaningful mission and the opportunity to work on high-impact products that are already defining the future of education
Opportunities to connect through affinity, ally, and social groups
401(k) + 4% matching & comprehensive insurance, including medical, dental, vision, and life.

Fulltime

Senior ML Engineer

We take pride in our commitment to excellence and our dedication to providing ta...

Location

Salary:

Not provided

Ennova Research

Expiration Date

Until further notice

Requirements

At least 5 years of production experience working in Data Science and Software Engineering
Understanding of data structures, data modeling and software architecture
Fluent in a at least two mainstream programming language (Python, Scala, Java, C++)
Experience developing/deploying ML solutions in one of the public cloud platforms (Google preferred)
Willingness to develop software applications exploiting the latest Generative AI technologies
Experience with deployment including knowledge of CI/CD, containerization, and related concepts
Ability to train junior team members in multiple Machine Learning and Deep Learning concepts

What we offer

Competitive salary package
Professional growth opportunities
Training program
Teamwork and participation in team building activities
A dynamic, young, creative and stimulating environment
Open-minded and multicultural work environment

Senior ML Platform Engineer

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP e...

Location

United States , Boston

Salary:

150000.00 - 210000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s Degree in Computer Science, Engineering, or a related field
or equivalent practical experience
5+ years of experience in software engineering with a focus on ML infrastructure, cloud platforms, or MLOps
Strong programming skills in Python, with experience in building distributed systems and REST/gRPC APIs
Deep knowledge of cloud-native services and infrastructure-as-code (e.g., AWS CDK, Terraform, CloudFormation)
Hands-on experience with model deployment platforms such as AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks
Proficiency in ML lifecycle tools (MLflow, Weights & Biases, BentoML) and containerization strategies (Docker, Kubernetes)
Understanding of data engineering and ingestion pipelines, with ability to interface with data lakes, feature stores, and streaming systems
Proven ability to work cross-functionally with Data Science, Data Platform, and Software Engineering teams, influencing decisions and driving alignment
Passion for AI and automation to solve real-world problems and improve operational workflows

Job Responsibility

Architect, build, own, and operate scalable ML infrastructure in cloud environments (e.g., AWS), optimizing for speed, observability, cost, and reproducibility
Create, support, and maintain core MLOps infrastructure (e.g., MLflow, feature store, experiment tracking, model registry), ensuring reliability, scalability, and long-term sustainability
Develop, evolve, and operate MLOps platforms and frameworks that standardize model deployment, versioning, drift detection, and lifecycle management at scale
Implement and continuously maintain end-to-end CI/CD pipelines for ML models using orchestration tools (e.g., Prefect, Airflow, Argo Workflows), ensuring robust testing, reproducibility, and traceability
Partner closely with Data Science, Sensor Intelligence, and Data Platform teams to operationalize and support model development, deployment, and monitoring workflows
Build, manage, and maintain both real-time and batch inference infrastructure, supporting diverse use cases from physiological analytics to personalized feedback loops for WHOOP members
Design, implement, and own automated observability tooling (e.g., for model latency, data drift, accuracy degradation), integrating metrics, logging, and alerting with existing platforms
Leverage AI-powered tools and automation to reduce operational overhead, enhance developer productivity, and accelerate model release cycles
Contribute to and maintain internal platform documentation, SDKs, and training materials, enabling self-service capabilities for model deployment and experimentation
Continuously evaluate and integrate emerging technologies and deployment strategies, influencing WHOOP’s roadmap for AI-driven platform efficiency, reliability, and scale

What we offer

equity
benefits

Fulltime

Senior ML Operations Engineer

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP e...

Location

United States , Boston

Salary:

150000.00 - 210000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Bachelor’s Degree: A degree in Computer Science, Software Engineering, or a related field
or equivalent practical experience
5+ years of experience in software engineering, with a significant focus on building and maintaining ML infrastructure in cloud environments
Deep expertise in AWS services, including but not limited to SageMaker, Lambda, ECS, S3, and IAM, with the ability to design and optimize cloud-based ML infrastructure
Strong programming skills in languages such as Python or Java, with a focus on building robust, maintainable code
Proven experience in productionalizing ML models, including building APIs and services that enable real-time inference
Expertise in designing scalable, resilient cloud architectures that support large-scale ML operations
Strong understanding of microservices, distributed systems, and the challenges of deploying and maintaining ML models in production environments
Excellent collaboration skills, with the ability to work closely with Data Scientists, AI and Software teams, and other cross-functional stakeholders
Agile Methodologies: Experience working in Agile/Scrum environments, with a focus on rapid iteration and continuous improvement

Job Responsibility

Design, develop, and maintain cloud-based infrastructure to support the deployment and scaling of machine learning models
Implement automated pipelines for continuous integration and continuous deployment (CI/CD) of ML models, ensuring seamless transitions from development to production environments
Collaborate closely with Data Scientists and AI teams to understand model requirements and facilitate the transition from prototype to production
Develop APIs, microservices, and other components necessary to integrate ML models into existing systems, enabling real-time inference and decision-making
Leverage cloud services to optimize the deployment and performance of machine learning models and associated infrastructure
Utilize services such as AWS SageMaker, Lambda, and ECS to build scalable, cost-effective solutions that support real-time ML/AI workloads
Monitor and optimize the performance of ML models in production, addressing issues related to latency, scalability, and resource utilization
Act as a key technical partner to Data Scientists, providing guidance on best practices for model deployment, versioning, and infrastructure design
Support AI teams by troubleshooting and resolving technical challenges related to model deployment and performance in production
Stay up-to-date with the latest advancements in ML infrastructure, cloud computing, and AI deployment strategies

What we offer

competitive base salaries
meaningful equity
benefits
generous equity package

Fulltime

Senior ML Engineer

The Muse

Location:
India , Bangalore

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
January 15, 2026

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