Senior ML Operations (MLOps) Engineer Job at Eight Sleep

Senior Engineer / Lead Engineer - Virtual Engineering - AI ML

Sponsorship:  GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE....

Location

India , Bengaluru

Salary:

Not provided

General Motors

Expiration Date

Until further notice

Requirements

Bachelor's or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar
5+ years in Automotive Manufacturing / Manufacturing Engineering Experience
1+ year experience in implementing AI/ML solutions in Automotive use cases
Should have executed at least 2 end-to-end projects in the text or Image data domain (from problem definition to deployment)
Strong programming skills in Python
Proficiency with ML/DL frameworks like Scikit-learn, TensorFlow, PyTorch, XGBoost
Solid understanding of statistics, probability, and linear algebra
Experience in data preprocessing, feature engineering, ETL and Exploratory Data Analysis (EDA)
Experience with MLOps platforms (MLflow, Kubeflow, Vertex AI, Azure ML)
Knowledge of ML model evaluation

Job Responsibility

Collaborate with stakeholders to understand business problems in the in the Manufacturing Engineering and Operations space and solve them using ML methodologies
Design, develop, and fine-tune AI/ML models for classification, regression, clustering, and recommendation systems
Work with MLOps tools to automate workflows, CI/CD pipelines, and model monitoring
Evaluate, validate, and benchmark model performance using appropriate metrics
Deploy AI models into production environments in collaboration with IT/AI teams
Establish monitoring and maintenance processes to ensure model accuracy over time
Ensure that all AI solutions comply with organizational data security, confidentiality, and regulatory requirements
Document workflows, results, and lessons learned for organizational knowledge sharing
Stay updated on advancements in ML model evaluation, ML frameworks, end-to-end ML pipelines

Fulltime

Senior MLOps Engineer - Data Ingestion - Paris

We are looking for a Senior MLOps Engineer to join the Panda Team (Data & ML Ope...

Location

France , Paris

Salary:

Not provided

Doctolib

Expiration Date

Until further notice

Requirements

You have at least 7+ years as an MLOps Engineer or ML Platform Engineer with proven production model lifecycle management experience
You have expert-level experience with ML orchestration tools (MLflow, Braintrust, or similar) for batch processing and inference pipelines
You have a strong Site Reliability Engineering (SRE) foundation with focus on operations excellence, reliability, and observability
You have expertise in Python for automation and ML pipeline scripting
You have strong proficiency with infrastructure-as-code tools such as Terraform and container orchestration (Kubernetes)
You have experience with model evaluation frameworks and golden dataset management
You have a solid understanding of cloud infrastructure (preferably GCP, AWS, or Azure)
You have excellent problem-solving skills with focus on identifying and resolving infrastructure bottlenecks
You are fluent in English

Job Responsibility

Design and implement end-to-end ML model pipelines in production (LLM and custom models) with robust deployment, evaluation, and monitoring frameworks
Own data pseudo-anonymization architecture within ingestion services, converting Tier 0 (personal identifiers) to Tier 1 (anonymized data) while ensuring data quality and model performance
Build and maintain secure data export services with ML-based threat detection to prevent attack vectors (SQL injection, etc.) using adaptive models rather than manual rules
Manage golden datasets and implement production model evaluation frameworks to ensure anonymization quality and system reliability
Build and maintain data pipelines that efficiently extract, transform, and load data from various sources, handling multiple data formats (text, images, audio, video)
Implement automation and orchestration tools using ML orchestration platforms (MLflow, Braintrust, or similar) to streamline infrastructure provisioning and reduce manual effort
Monitor data and ML platforms for performance, reliability, and security
identify and troubleshoot issues proactively
Mentor team members on MLOps expertise and best practices to reduce knowledge silos and build organizational capability

What we offer

Free comprehensive health insurance for you and your children
25 days of paid vacation per year, plus up to 14 days of RTT
Free mental health and coaching services through our partner Moka.care
Work from abroad for up to 10 days per year thanks to our flexibility days policy
Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib
A subsidy from the work council to refund part of the membership to a sport club or a creative class
50% reimbursement of your public transport subscription
Parent Care Program: receive one additional month of leave on top of the legal parental leave
For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
Relocation support in case of international mobility

Fulltime

Senior MLOps Engineer

We're looking for an MLOps Engineer to help ensure our machine learning models r...

Location

Denmark , København

Salary:

Not provided

Life Science Talent

Expiration Date

Until further notice

Requirements

Experience deploying machine learning models into production and managing their lifecycle
Experience implementing model governance, including versioning, monitoring, drift detection, and reporting
Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC
Solid understanding of CI/CD systems (e.g., GitHub Actions, ArgoCD) and infrastructure-as-code tools (e.g., Terraform, Helm)
Familiarity with data engineering concepts such as ETL pipelines, data lakes, and large-scale batch/stream processing
Experience mentoring or supporting colleagues to help them grow their technical skills
Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role
Strong proficiency in Python
Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes)
Ability to design scalable, secure, and observable systems in fast-moving environments

Job Responsibility

Own and manage the full lifecycle of both ML models and core infrastructure – from development and deployment to monitoring and continuous improvement
Build and maintain robust CI/CD pipelines for both software and ML workflows
Ensure reliability, scalability, observability, and security of production systems and ML infrastructure
Automate deployment, orchestration, and environment management using modern DevOps tooling
Collaborate closely with software engineers, ML engineers, and product teams to bring ML-powered features to production
Proactively detect, troubleshoot, and resolve infrastructure and model performance issues
Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering
Document infrastructure, workflows, and operational procedures clearly and thoroughly

What we offer

Equipment provided by Corti

Fulltime

Senior ML Engineer- Distillation

A seasoned Senior ML Engineer who drives distillation of ML Models for high-perf...

Location

Poland , Warsaw

Salary:

Not provided

AMD

Expiration Date

Until further notice

Requirements

6–10+ years in ML engineering or applied research
3+ years focused on model distillation/compression at production scale
Strong proficiency in PyTorch (preferred) or JAX/TF
Ability to implement custom training loops, distributed training, and mixed precision
Demonstrated experience shipping distilled or compressed models to production with measurable gains in latency/memory and maintained quality
Deep understanding of knowledge distillation techniques: teacher–student frameworks, soft-labels, intermediate feature matching, contrastive distillation, task-specific loss shaping
Hands-on experience with quantization (static/dynamic, PTQ/QAT), pruning, and graph-level optimizations (operator fusion)
GPU performance engineering: CUDA fundamentals, TensorRT/ONNX Runtime, kernel profiling (Nsight), memory/layout optimization
Solid grasp of computer graphics fundamentals: rendering pipeline, shaders, sampling, anti-aliasing, tone mapping, and perceptual metrics
Strong software engineering: Python/C++ proficiency, testing, code quality, version control, reproducible pipelines, containerization

Job Responsibility

Distillation and compression: KD variants, hint/fitnets, attention transfer, feature mimicking, low-rank/SVD, sparsity
Efficient architectures: MobileNet/EfficientNet, vision transformers optimization, lightweight diffusion/UNet variants, NeRF/instant-NGP distillation
Inference optimization: TensorRT, CUDA, cuDNN, ONNX, quantization-aware training, weight clustering, operator fusion
Metrics: SSIM, LPIPS, PSNR, FID/KID, latency/throughput profiling, memory/activation footprint analysis
Data and training: large-scale dataset curation, synthetic data generation, curriculum learning, augmentation strategies
MLOps: experiment tracking, CI/CD for models, model registries, reproducibility, telemetry
Integrate ML inference into production rendering pipelines: define model I/O, preprocessing/postprocessing, and make trade-offs for latency, throughput, and quality
Collaborate across teams (ML researchers, engine/platform, tooling, QA) to translate ML and product requirements into graphics-friendly implementations and integration plans
Mentor other engineers, conduct code reviews, and help define best practices for rendering, performance, and SDK delivery

Senior ML Engineer

We are seeking an experienced Senior ML to join our team and engage in a diverse...

Location

United Kingdom , Bath

Salary:

Not provided

BMT

Expiration Date

Until further notice

Requirements

Be a UK sole national
Have held no other nationality at any time
Have continuously resided in the United Kingdom for the past five years
Be able to obtain and maintain full UK security clearance in accordance with government vetting standards
Provide satisfactory evidence of identity, nationality, and residency as part of the clearance process
Capability to design and implement end‑to‑end ML pipelines
Ability to select, train, and tune models (classical ML and deep learning) using frameworks such as PyTorch, TensorFlow, or scikit‑learn
Experience containerising and deploying models (e.g., Docker), implement CI/CD, monitoring, drift detection, and automated retraining on Azure/AWS/GCP as appropriate
Demonstrated capability to work with data engineers to ensure high‑quality datasets, versioning, lineage, and governance
Capable of pairing with data scientists and software engineers, review code, and share best practices

Job Responsibility

Design, build, and deployment of machine‑learning systems, applying robust software engineering practices and an in‑depth understanding of model behaviour, performance, and limitations
Select, prepare, and pipeline data for model training and inference. Implements, trains, evaluates, and optimises machine‑learning models, continually improving them through iterative experimentation and additional data
Create scalable and automated ML pipelines, including feature extraction, model training, validation, packaging, deployment, and monitoring
Design and implement dashboards, diagnostics, and evaluation tooling to ensure transparency, performance tracking, and operational reliability across the ML lifecycle
Within defined delivery goals, refines prototype models into production‑ready components, contributing to development, optimisation, demonstration, and integration activities
Apply standardised engineering and evaluation methods, producing clear technical documentation and communicating design choices, performance outcomes, and limitations
Contribute to internal knowledge bases and participates in professional ML engineering communities
Ensure responsible handling of data throughout the ML lifecycle, including secure storage, access control, data lineage, versioning, and quality checks
Evaluate data integrity and suitability for ML workflows, and advises on transformations, feature representation, and schemas needed for efficient training and inference
Implement metadata standards, reproducible data pipelines, and automated validation procedures to maintain trustworthy data assets

What we offer

Private Medical (family coverage)
Enhanced Pension
18 weeks enhanced maternity pay (after a qualifying period of 1 year)
Family friendly policies
Committed to an inclusive culture
Wellbeing Fund – an annual fund for personal hobbies or interests
26 Days Annual Leave (plus bank holidays)
Holiday Trading
Retail Vouchers
Professional Subscriptions

Fulltime

Senior MLOps Engineer

As an MLOps Engineer in DAMO service line, you will be responsible for ensuring ...

Location

Singapore , Singapore

Salary:

Not provided

Thoughtworks

Expiration Date

Until further notice

Requirements

High proficiency in Python (Pandas, NumPy, Scikit-learn) for scripting, analysis, and maintaining production models
Strong SQL skills for querying, data manipulation, and operational data checks
Experience building or maintaining GenAI / agentic solutions (e.g., RAG, LlamaIndex, CrewAI, or similar orchestration/RAG tooling)
Solid understanding of classical ML algorithms, model evaluation, and challenges like drift and bias
Hands-on experience with model monitoring (data quality, prediction quality, latency) using Prometheus, Grafana, or cloud-native tools
Experience with Azure (Databricks, Azure Machine Learning, etc.) for deployment and resource management
Familiarity with Agile methodologies (Scrum/Kanban)
Must be Singaporean citizens or already hold Singaporean Permanent Residency (PR) at the time of application
Willingness to be part of a 24x7 on-call rotation, as needed

Job Responsibility

Design, implement and maintain monitoring and alerting for ML and AI operational signals
Build and operate robust evaluation and testing pipelines for all ML and AI systems
Investigate and resolve production issues related to model behaviour
Collaborate with infrastructure and platform teams to ensure stable, performant and cost-efficient AI inference
Manage the lifecycle of ML models, prompts, embeddings, vector indices and associated components
Design and operate effective feedback loops that incorporate real user interactions
Uphold governance, safety and compliance standards
Maintain clear, comprehensive documentation
Communicate system health, risks, upcoming changes and operational insights clearly
Support the growth and development of junior team members

What we offer

Learning & Development
Interactive tools
Numerous development programs
Teammates who want to help you grow

Fulltime

Senior MLOps / LLMOps Engineer

Location

Germany , Berlin

Salary:

Not provided

ImmoScout24 GmbH

Expiration Date

Until further notice

Requirements

Strong experience in MLOps (CI/CD, Docker, Kubernetes) and operating production-grade systems
Proficiency in Python and solid software engineering and scalable system design skills
Hands-on experience with LLMs and generative AI technologies (e.g. GPT, Gemini or Anthropic-like models)
Expertise in prompt engineering, agent orchestration, context management, and output validation
Experience with LLM evaluation frameworks and deploying self-hosted LLMs
Familiarity with cloud platforms (e.g. AWS, GCP) as well as DevOps, testing, and observability practices
Strong communication skills and ability to collaborate with cross-functional teams and stakeholders

Job Responsibility

Design and maintain scalable ML/LLM infrastructure and pipelines
Productionize traditional ML and generative AI solutions with cross-functional product teams
Own the ML/LLMOps lifecycle: prompting, deployment, monitoring, evaluation and optimization
Build and evolve an LLM Gateway service to standardize access, routing, and governance
Develop evaluation frameworks to measure quality, performance, and reliability of LLM outputs
Design and implement MCP-compatible services to enable standardized context exchange between LLMs, tools, and data sources
Integrate MCP into internal platforms to support tool use, retrieval, and agent-based workflows across teams
Work with AWS and integrate self-hosted open-source AI models for scalable, secure applications
Ensure observability, cost efficiency, and system performance
Contribute to project management, stakeholder communication and cross-team collaboration

What we offer

A competitive salary package and a bonus on top
Hybrid work model with three days of on-site work per week in the office
30 days of vacation per year
Possibility to work from abroad for 10 days per year
Relocation agency support with visa process and attractive relocation package
Plus membership for tenants on ImmoScout24
Dedicated learning time per month, online courses on ScoutAcademy, regular book challenges, structured feedback, Lunch & Learn events and individual career paths
Professional family service for childcare
Bring your dog to work (upon approval)
Subsidized public transport or Job Bikes

Senior ML Infrastructure Engineer - Embodied AI

At General Motors, our product teams are redefining mobility. Through a human-ce...

Location

United States , Sunnyvale

Salary:

153200.00 - 234100.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

3+ years of experience working on large-scale distributed systems, applications, or ML infrastructure
Experience designing robust services or frameworks with durable, well-designed APIs
Solid understanding of machine learning workflows and hands-on experience applying ML systems in production environments
Experience building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure
Practical experience across the ML development lifecycle, including model training, deployment, and MLOps practices
Strong cross-functional collaboration skills across teams and organizations
Strong coding skills in Python or C++
Interest in autonomous driving and large-scale ML systems
BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience

Job Responsibility

Design, implement, and deploy scalable platforms and tools supporting machine learning training and evaluation workflows across GM
Drive complex technical projects with strong ownership of implementation, code quality, and system reliability
Contribute to technical design discussions and architectural decisions while collaborating with senior engineers and technical leads
Work closely with partner teams to ensure platforms meet real-world ML development needs and maximize adoption
Identify technical improvements and help prioritize platform investments to improve performance, reliability, and developer productivity
Contribute to a strong engineering culture through high-quality code reviews, documentation, and operational excellence
Support onboarding and mentoring of junior engineers and interns

What we offer

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs

Fulltime

Select Country

Senior ML Operations (MLOps) Engineer

Job Description

Job Responsibility

Requirements

Nice to have

What we offer

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