Senior ML Engineer Jobs

New

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

New

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)

New

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

New

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

New

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 AI ML Engineer

We are seeking a highly skilled and experienced Assistant Vice President (AVP), ...

Location

India , Pune

Salary:

Not provided

Citi

Expiration Date

Until further notice

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field
Minimum of 6+ years of professional experience in Data Science, Machine Learning Engineering, or a similar role, with a strong track record of deploying ML models to production
Proven experience in a lead or senior technical role
Expert-level proficiency in Python programming, including experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn) and deep learning frameworks (e.g., TensorFlow, PyTorch)
Strong hands-on experience designing, developing, and deploying RESTful APIs using FastAPI
Solid understanding and practical experience with CI/CD tools and methodologies (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps) for MLOps
Experience with MLOps platforms, model monitoring, and model versioning
Experience with at least one major cloud provider (e.g., AWS, Azure, GCP) for deploying and managing ML workloads
Proficiency in SQL and experience working with relational and/or NoSQL databases
Deep understanding of machine learning algorithms, statistical modeling, and data mining techniques

Job Responsibility

Design, develop, and implement advanced machine learning models (e.g., predictive, prescriptive, generative AI) to solve complex business problems, from initial data exploration and feature engineering to model training and evaluation
Lead the deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance
Build and maintain robust, high-performance APIs (using frameworks like FastAPI) to serve machine learning models and integrate them with existing applications and systems
Establish and manage continuous integration and continuous deployment (CI/CD) pipelines for ML code and model deployments, promoting automation and efficiency
Collaborate with data engineers to ensure optimal data pipelines and data quality for model development and deployment
Conduct rigorous experimentation, A/B testing, and model performance monitoring to continuously improve and optimize AI/ML solutions
Promote and enforce best practices in software development, including clean code, unit testing, documentation, and version control
Mentor junior team members, contribute to technical discussions, and drive the adoption of new technologies and methodologies within the team
Effectively communicate complex technical concepts and model results to both technical and non-technical stakeholders.

What we offer

Not explicitly stated.

Fulltime

New

Senior Software Engineer – ML Model Compliance & Automation

We are seeking a highly skilled and motivated Senior Software Engineer to lead t...

Location

India , Jaipur

Salary:

Not provided

InfoObjects

Expiration Date

Until further notice

Requirements

Experience Required: 3 - 7 yrs
GoLang (preferred)
Python (preferred)
Bash
MLOps Tools: KitOps, MLModelCI, MLflow, ONNX, TensorFlow, PyTorch, Docker
SBOM & Security: Syft, Grype, Trivy, CycloneDX, SPDX
CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD
Infra: Kubernetes, Docker, Helm, Terraform
Cloud: AWS, GCP, Azure (EKS/GKE/ECS preferred)
Version Control: Git, GitOps

Job Responsibility

Model Packaging & Artifact Management: Design and implement workflows for packaging ML models using KitOps, ONNX, MLflow, or TensorFlow SavedModel
Manage model artifact versioning, registries, and reproducibility
Ensure artifact integrity, consistency, and traceability across CI/CD pipelines
Model Profiling & Optimization: Automate model profiling (latency, size, ops) using MLModelCI, TorchServe, or ONNX Runtime
Apply quantization, pruning, and format conversions (e.g., FP32→INT8) for optimization
Embed profiling and optimization checks into CI/CD pipelines to assess deployment readiness
Compliance & SBOM Generation: Develop pipelines to generate and validate SBOMs for ML models
Implement compliance checks for licensing, vulnerabilities, and security using CycloneDX, SPDX, Syft, or Trivy
Validate schema, dependencies, and runtime environments for production readiness
Cloud Integration & Deployment: Automate model registration, endpoint creation, and monitoring setup in AWS/GCP/Azure

Fulltime

New

Senior AI ML Engineer

We build breakthrough software products that power digital businesses. We are an...

Location

Noida

Salary:

Not provided

3Pillar Global

Expiration Date

Until further notice

Requirements

Python
LLM Application Development (Dify, Autogen Studio or similar, etcetera)
Frameworks such as LangChain, LlamaIndex, Klu.AI
Workflow & Agents
Prompt Engineering
LLM Implementation and Integration
API Development
AI Ethics

What we offer

flexible work environment (office, home, or blend)
remote-first approach
part of a global team
well-being focus (fitness offerings, mental health plans, generous time off)
accelerated career growth and development opportunities
equal opportunity employer
values like Intrinsic Dignity and Open Collaboration

Fulltime

Senior ML Engineer

Ennova Research

Location:

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Requirements:

Additional Information:

Job Posted:
December 08, 2025

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