ML Model Serving Engineer Job at Sesame (San Francisco)

ML Engineer (Production-focused)

We are looking for an ML Engineer with hands-on experience bringing models into ...

Location

France , Paris

Salary:

Not provided

Corsearch

Expiration Date

Until further notice

Requirements

3+ years of experience as an ML Engineer delivering models into production
Strong programming skills in Python (production-level)
Hands-on experience with PyTorch (preferred) or TensorFlow
Proven experience in model deployment / model serving
Experience optimizing inference (latency, resource usage, throughput)
Strong understanding of ML pipelines and automated workflows
Experience with Docker and containerized ML workloads
Ability to demonstrate measurable impact (e.g., uplift, precision improvements, latency reduction, stability gains)
Fluent spoken and written English
Located within a time zone aligned with CET (CET −2 to CET +4)

Job Responsibility

Build and maintain ML models for large-scale detection, classification, and automation tasks
Optimize inference performance (latency, throughput, memory)
Develop and maintain end-to-end ML pipelines: data processing, training, validation, deployment, monitoring
Integrate ML components into microservice-based architecture
Work closely with engineering teams to ensure reliability and performance in production
Improve tooling for model versioning, testing, and CI/CD

ML Platform Engineer

We are seeking a Machine Learning Engineer to help build and scale our machine l...

Location

United States

Salary:

Not provided

Duetto

Expiration Date

Until further notice

Requirements

3+ years of experience in ML engineering or a similar role building and deploying machine learning models in production
Strong experience with AWS ML services (SageMaker, Lambda, EMR, ECR) for training, serving, and orchestrating model workflows
Hands-on experience with Kubernetes (e.g., EKS) for container orchestration and job execution at scale
Strong proficiency in Python, with exposure to ML/DL libraries such as TensorFlow, PyTorch, scikit-learn
Experience working with feature stores, data pipelines, and model versioning tools (e.g., SageMaker Feature Store, Feast, MLflow)
Familiarity with infrastructure-as-code and deployment tools such as Terraform, GitHub Actions, or similar CI/CD systems
Experience with logging and monitoring stacks such as Prometheus, Grafana, CloudWatch, or similar
Experience working in cross-functional teams with data scientists and DevOps engineers to bring models from research to production
Strong communication skills and ability to operate effectively in a fast-paced, ambiguous environment with shifting priorities

Job Responsibility

Develop, maintain, and scale machine learning pipelines for training, validation, and batch or real-time inference across thousands of hotel-specific models
Build reusable components to support model training, evaluation, deployment, and monitoring within a Kubernetes- and AWS-based environment
Partner with data scientists to translate notebooks and prototypes into production-grade, versioned training workflows
Implement and maintain feature engineering workflows, integrating with custom feature pipelines and supporting services
Collaborate with platform and DevOps teams to manage infrastructure-as-code (Terraform), automate deployment (CI/CD), and ensure reliability and security
Integrate model monitoring for performance metrics, drift detection, and alerting (using tools like Prometheus, CloudWatch, or Grafana)
Improve retraining, rollback, and model versioning strategies across different deployment contexts
Support experimentation infrastructure and A/B testing integrations for ML-based products

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

Senior Software Engineer - Network Enablement (Applied ML)

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

Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred)
Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark
Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference
Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics
Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline & online parity, monitoring and incident response

Job Responsibility

Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows)
Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact)
Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses
Build and operate offline training pipelines and production batch scoring for bank intelligence products
Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring
Implement model CI/CD, model/version registry, and safe rollout/rollback strategies
Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs
Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions
Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection)
Ensure fairness, explainability and PII-aware handling for partner-facing ML features

What we offer

medical
dental
vision
401(k)
equity
commission

Fulltime

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

Principal Engineer - Marketplace

Principal Engineer role in the Marketplace Engineering team to lead breakthrough...

Location

United States , San Francisco; Sunnyvale

Salary:

302000.00 - 336000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master’s degree with 12+ years of industry experience
10+ years of experience building and deploying ML models in large-scale production environments
Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch, JAX) and distributed computing platforms (Spark, Ray)
Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, Algorithmic Game Theory, and Large-scale Ads Ranking/Auction Systems
Proven track record of leading complex ML projects from research through production with significant measurable business impact
Strong programming skills in Python, Java, or Go with experience building production ML systems
Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
Advanced Deep Learning and Neural Network architectures
Scalable ML architecture and distributed model training

Job Responsibility

Lead the design and implementation of advanced ML systems for dynamic pricing algorithms serving millions of drivers across 70+ countries around the world
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency requirements
Drive breakthrough research in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization with strategic agents
Own end-to-end ML model lifecycle from research through production deployment and continuous optimization
Develop and enforce best practices in system design, ensuring data integrity, security, and optimal performance
Serve as a representative for the Marketplace organization to the broader internal and external technical community
Contribute to the eng brand for Marketplace and serve as a talent magnet to help attract and retain talent for the team
Stay abreast of industry trends and emerging technologies in software engineering, focused particularly on ML/AI, to enhance our systems and processes continually
Build scalable ML architecture and feature management systems supporting Driver Pricing and broader Marketplace teams
Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other experimental methodologies

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
Eligible to participate in a 401(k) plan
Eligible for various benefits (details at provided link)

Fulltime

AI Product Manager

We’re scaling AI and machine learning across our products, devices, and operatio...

Location

United States , Boston

Salary:

121300.00 - 177900.00 USD / Year

SimpliSafe

Expiration Date

Until further notice

Requirements

8+ years of product management experience, including significant ownership of AI/ML or data-intensive products
Clear track record of shipping production ML systems (not just integrating third-party AI APIs), in close partnership with data science, ML engineering, and MLOps
Principal-level impact: leading cross-team initiatives, shaping strategy, and influencing senior stakeholders
Strong understanding of core ML concepts and lifecycle: data, labeling, training/validation, evaluation metrics, deployment, monitoring, and retraining
ML experience with at least one of following: computer vision or sensor data, LLM-powered applications (prompting, RAG, fine-tuning, evaluation) and/or hardware or edge products (e.g., on-device models, connectivity/latency trade-offs)
Familiarity with modern ML infrastructure (cloud platforms, model serving, CI/CD for ML, monitoring/alerting)
Comfortable going deep into data, metrics, and model behavior—not just the UX layer
Excellent communicator who can make complex AI topics clear to diverse audiences
Strong alignment with our values: customer-obsessed, low ego, highly collaborative, comfortable with ambiguity, and biased toward learning and iteration.

Job Responsibility

Define and communicate the multi-year roadmap for key AI/ML capabilities across SimpliSafe
Identify and prioritize AI opportunities where models and data can materially improve safety, customer experience, or efficiency—on both devices and cloud services
Make build-vs-buy decisions for AI capabilities in partnership with data science and engineering
Partner with data scientists, ML engineers, and MLOps to design and deliver end-to-end ML solutions—from problem framing through data, training, evaluation, deployment, and monitoring
Work with hardware and embedded teams to shape edge AI/ML experiences (e.g., on-device detection, low-latency decisions, bandwidth-aware designs)
Define model-level requirements (metrics, latency, cost, guardrails) and connect them to business outcomes (e.g., false alarm reduction, detection accuracy, handle time, CSAT)
Translate product needs into requirements for ML platform capabilities (model serving, observability, experiment tracking, human-in-the-loop tools)
Lead product direction for LLM and multimodal use cases (e.g., text, vision, sensor data)
Decide when to use prompt engineering, RAG, fine-tuning, or traditional ML—and how to evaluate quality, safety, and hallucinations
Design workflows that incorporate human review and escalation where needed

What we offer

A mission- and values-driven culture and a safe, inclusive environment where you can build, grow, and thrive
A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families
Free SimpliSafe system and professional monitoring for your home
Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change
Participation in our annual bonus program, equity, and other forms of compensation, in addition to a full range of medical, retirement, and lifestyle benefits.

Fulltime

Senior Staff Machine Learning Engineer - Driver Pricing & Marketplace Optimization

We’re seeking an exceptional Senior Staff ML Engineer to lead breakthrough ML in...

Location

United States , Sunnyvale, California; San Francisco, California

Salary:

267000.00 - 297000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master’s degree with 12+ years of industry experience
10+ years of experience building and deploying ML models in large-scale production environments
Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch, JAX) and distributed computing platforms (Spark, Ray)
Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, Algorithmic Game Theory, and Large-scale Ads Ranking/Auction Systems
Proven track record of leading complex ML projects from research through production with significant measurable business impact
Strong programming skills in Python, Java, or Go with experience building production ML systems
Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives

Job Responsibility

Lead the design and implementation of advanced ML systems for dynamic pricing algorithms serving millions of drivers across 70+ countries around the world
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency requirements
Drive breakthrough research in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization with strategic agents
Own end-to-end ML model lifecycle from research through production deployment and continuous optimization
Build scalable ML architecture and feature management systems supporting Driver Pricing and broader Marketplace teams
Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other experimental methodologies
Establish ML engineering best practices, monitoring, and operational excellence across the organization
Create platform abstractions that enable other ML engineers to iterate faster on pricing algorithms
Partner with Product, Operations, and Earner Experience teams to translate complex business requirements into ML solutions
Collaborate with Marketplace Engineering and Science teams to productionize cutting-edge ML research

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
Eligible for various benefits

Fulltime

ML Model Serving Engineer

Sesame

Location:
United States , San Francisco ▼
New York
Bellevue

Category:
IT - Software Development

Contract Type:
Employment contract

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 20, 2026

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