Model Monitoring

Model Monitoring Data Jobs

3 jobs found

30d ago

Principal Machine Learning Engineer

On behalf of Next Deavor New York, NY

New York, NY

30d ago Closed

$200k - $250k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

This position is no longer accepting applications (closed Sep 13, 2026).

Job Description

Hybrid in the New York City Metro area (3 days onsite). In this Principal Machine Learning Engineering role, you will own the ML infrastructure behind real-time compliance enforcement systems. Expect a position centered on building the pipelines, evaluation workflow, and production serving needed to train, measure, and deploy models reliably, with a focus on latency, reliability, and cost.

Pay range: USD $200,000 - $250,000 per year.

What you’ll do

Build and own training pipelines including data preparation, reproducible fine-tuning runs, experiment tracking, and release automation.
Develop evaluation infrastructure with automated eval runs, regression gates, dashboards, and dataset versioning.
Own production model serving for low-latency inference, including batching, optimization, autoscaling, and cost management.
Ship model updates safely using versioning, canarying, rollback, and drift monitoring.
Create repeatable workflows to adapt models to new domains and changing customer needs.
Convert expert labels and reviewer feedback into clean training and evaluation datasets.
Help raise the team’s engineering bar for ML infrastructure as the organization grows.

What you’ll bring

8+ years of software engineering experience, including 4+ years building ML or LLM infrastructure for production.
Hands-on experience with the modern LLM stack: PyTorch, distributed training, and fine-tuning at scale (e.g., LoRA, SFT) using inference engines such as vLLM or TensorRT-LLM.
Experience building eval harnesses, regression gates, or dataset pipelines, with strong understanding of precision, recall, and calibration.
Proven ownership of production model serving with real latency, reliability, and cost constraints.
Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, and observability.
Ability to scope work, ship frequently, and make pragmatic build-vs-buy decisions.
Experience collaborating tightly with research partners and defining clear interfaces.

Technologies you’ll work with

PyTorch, LoRA, SFT, vLLM, TensorRT-LLM
Python, containers, CI/CD, cloud infrastructure, observability

Additional qualifications that may help

Experience productionizing small or specialized language models.
Experience with structured-output serving or constrained decoding in production.
Prior work in regulated or high-stakes domains (fintech, healthcare, legal, trust and safety).
Experience deploying models into customer-controlled environments.

Work location: Hybrid remote in New York, NY 10001 (3 days onsite).

This role may fill quickly. Submit your resume to be considered.

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1mo ago

Machine Learning Engineer

Mariana Minerals Houston, TX

Houston, TX

1mo ago Closed

$120k - $180k/yr Full time Ai Ml Artificial Intelligence Automation

This position is no longer accepting applications (closed Sep 7, 2026).

Job Description

Mariana Minerals is building machine learning systems that help control mineral refining facilities and move toward fully autonomous refining operations. You will work on reinforcement learning in physically realistic simulators, train control models, and help connect simulation results to real plant data so improvements make it into production.

Based in Houston, TX, this is an onsite role with a salary range of USD 120,000 - 180,000 per year. Experience level starts at 2+ years.

What you’ll do

Run reinforcement learning experiments in physically realistic simulators of mineral processing operations, then translate results into better controllers.
Build and refine parts of training environments, including reward functions, observations, and action logic, with guidance from senior engineers.
Train control models, track and interpret performance, and investigate causes when a model underperforms.
Close the gap between simulation and reality by comparing model behavior against real plant data and flagging where physics diverges.
Write clean, well-tested code and contribute to services that help deploy models into production.
Partner with process and chemistry experts to understand the unit operations being modeled.

What makes the work challenging

Our internal platform uses reinforcement learning toolkits similar to those used in self-driving vehicles and humanoid robots, applied to autonomous, short-interval control of mineral refining circuits.
Models adjust operating set points and configurations in real time, optimizing across lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously.
Training environments are noisy and non-stationary, with shifting wastewater compositions, changing ore grades, and equipment aging, requiring continuous adaptation.
Training occurs inside physically realistic simulators of process units, followed by validation against real plant data before anything impacts live equipment.

What you bring

2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing, or a strong recent graduate with demonstrated project depth.
Solid grounding in machine learning fundamentals and working knowledge of modern deep learning; reinforcement learning exposure is a plus.
Proficiency in Python and comfort reading and debugging an existing codebase.
Curiosity about physical, industrial systems, with eagerness to learn chemistry and process engineering from experts who will challenge assumptions.
A self-starter who asks good questions, ships work, and escalates blockers early.

Why this role

Mariana Minerals owns the projects, generates the data, and closes the loop. Each facility built makes the software smarter and helps the next facility move faster and cost less. Mining is one of the last major industrial sectors that has not been rebuilt with modern software, so this role centers on creating workflows and systems that do not yet exist. Your work will directly influence how critical minerals are produced at scale in the coming decades.

How we work

Extreme Ownership: take full responsibility for outcomes and drive toward solutions.
Engineer Out Requirements, then Automate: simplify, optimize, and automate for scale.
Share Your Legos: collaborate openly, share knowledge, and empower each other to build bigger, better solutions.

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1mo ago

Machine Learning Engineer II

Tinder Los Angeles, CA

Los Angeles, CA

1mo ago Closed

$145k - $165k/yr Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Sep 1, 2026).

Job Description

Tinder is seeking a Machine Learning Engineer II for a hybrid role in Los Angeles, CA. The position focuses on building and shipping machine learning systems to improve product experiences and drive measurable business impact. It is an individual contributor role centered on modeling and algorithmic innovation, translating product opportunities into ML solutions, running experiments, and deploying models to production. Compensation for this role ranges from USD 145,000 to 165,000 per year.

Responsibilities

Translate product and business problems into clear machine learning problems with measurable success criteria
Build, train, evaluate, and improve production machine learning models
Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production
Design and analyze offline evaluations and online experiments to understand model impact
Contribute to feature engineering, data preparation, training pipelines, and model monitoring
Write clean, maintainable, production-quality code and participate in design and code reviews
Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners

Requirements

BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
1+ year of industry experience in machine learning, software engineering, data science, or a related field
Strong foundation in computer science fundamentals, including data structures, algorithms, and software design
Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated
Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language
Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation
Strong communication skills and the ability to collaborate effectively across functions
Self-motivated, proactive, and comfortable taking ownership of well-scoped problems

Technologies

Python
Java
Kotlin
Go
Scala
Spark
Flink
AWS
Kubernetes
TensorFlow Serving
TorchServe
Triton Inference Server
Ray Serve
Airflow

Benefits

Flexible Vacation, 10 Sick Days
Time off to volunteer and charitable donations matched up to $15,000 annually
Comprehensive health, vision, and dental coverage
100% 401(k) employer match up to 10%, Employee Stock Purchase Plan (ESPP)
100% paid parental leave (including for non-birthing parents) and family forming benefits
Investment in your development: mentorship through our MentorMatch program, access to 6,000+ online courses through Udemy, and an annual $3,000 stipend for your professional development
Investment in your wellness: access to mental health support via Modern Health, paid concierge medical membership, pet insurance, fitness membership subsidy, and commuter subsidy
Free subscription to Tinder Gold

Nice to Have

Experience with recommendation systems or casual inference
Familiarity with big data or stream processing frameworks such as Spark or Flink
Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes
Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve
Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow
Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation
Exposure to observability and monitoring for ML systems
Exposure to LLM-related use cases or applied generative AI projects

Where you'll work

This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, California.

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Model Monitoring Data Jobs

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