Machine Learning Engineer, AssetWatch
Predictive Maintenance
$151-175k
SQL
AWS
Python
Tensorflow
MySQL
Athena
Spark
NumPy
Pandas
Scikit-Learn
PyTorch
Sagemaker
RDS
Git
Junior, Mid and Senior level
Remote from US
Remote monitoring for proactive maintenance
Be an early applicant
Remote monitoring for proactive maintenance
201-500 employees
B2BArtificial IntelligenceDeep TechInternet of ThingsSaaSAutomation
Be an early applicant
$151-175k
SQL
AWS
Python
Tensorflow
MySQL
Athena
Spark
NumPy
Pandas
Scikit-Learn
PyTorch
Sagemaker
RDS
Git
Junior, Mid and Senior level
Remote from US
201-500 employees
B2BArtificial IntelligenceDeep TechInternet of ThingsSaaSAutomation
Company mission
To build the leading proactive maintenance platform for industrial organizations around the world.
Role
Who you are
- Master's or Ph.D. in Mechanical Engineering, Electrical Engineering, Computer Science/Engineering, or a related field
- Proven experience in Predictive Maintenance and Condition Monitoring with a focus on Signal Processing techniques such as FFT, Short Time Fourier Transform, Time Synchronous Averaging, Wavelet Transform, Spectral Kurtosis, Spectral Correlation, Envelope Analysis, Hilbert Transform, and related methods
- Strong foundations in Machine Learning and model development, including classical Machine Learning algorithms, supervised and unsupervised learning, anomaly detection, feature engineering, Artificial Neural Networks, Convolutional Neural Networks, Sequence-based Models such as RNNs and LSTMs, and Attention Mechanisms
- Strong understanding of time-series modeling and evaluation, including the ability to select appropriate techniques based on the physical problem, data characteristics, available ground truth, and operational requirements
- Hands-on experience architecting and delivering production ML solutions on AWS is required, including several of: SageMaker (Training, Processing, Pipelines, Model Registry, Endpoints), S3, Athena, AWS Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, IoT Core, CloudWatch, and IAM
- Demonstrated data and ML engineering at scale, including distributed processing with Spark (PySpark) or equivalent, efficient handling of large time-series and binary signal data, and pragmatic reasoning about compute cost, runtime, and reprocessing risk
- Experience designing data mining and annotation workflows in partnership with domain experts, including label taxonomy design, labeling tooling, label quality control, and converting noisy expert feedback into model-ready ground truth
- Strong software engineering and programming skills in Python, with experience developing modular, testable, maintainable production code; proficiency with Git, code review, unit/integration/regression testing, debugging, CI/CD, and reproducible development practices on noisy, large, imperfect industrial datasets
- Proficiency in scientific and ML/DL frameworks and libraries such as NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch, or similar tools
- Experience using Large Language Models, AI-assisted coding, and agentic AI tools for software and ML development; familiarity with designing or integrating LLM- or agent-based workflows for data processing, analysis, decision support, or automation is highly desirable
- Working knowledge of SQL (MySQL/Aurora, Athena) and the ability to independently explore, validate, and troubleshoot large and imperfect industrial datasets
- Experience building representative benchmark datasets, validating models against reviewed ground truth, analyzing false positives and false negatives, and creating repeatable evaluation or regression workflows is highly desirable
- Physics-informed ML and experience with additional modalities (electrical/current signature analysis, ultrasound, oil analysis) are a plus
- Excellent communication and teamwork skills, including the ability to collaborate with condition-monitoring domain experts, MLOps engineers, Data Engineers, Product, and other stakeholders
- Demonstrated research work in high-quality journals or significant applied work on related subjects is a plus
What the job involves
- Extract, preprocess, and analyze data from various sensor sources, primarily machine vibration, while also working with temperature, electrical, image, and machine-context data to identify and enhance diagnostic features and fault-specific signatures
- Determine the appropriate modeling techniques for each problem, including classical Machine Learning, supervised and unsupervised learning, anomaly detection, Artificial Neural Networks, Deep Learning, signal-processing methods, and hybrid or rules-based approaches
- Train, validate, and deploy predictive maintenance models to accurately identify and predict machinery faults such as bearing, gearbox, shaft, motor, pump, fan, and belt faults. This may include CNNs, RNNs, LSTMs, attention-based models, and other time-series or representation-learning approaches
- Develop and evaluate time-domain, frequency-domain, and contextual features, including harmonics, sidebands, envelope-spectrum features, running-speed characteristics, bearing and gear-mesh fault frequencies, and other fault-specific evidence
- Act as the solution architect for assigned ML initiatives: define the end-to-end design — data sources and contracts, batch vs. near-real-time execution, feature computation strategy, storage layout (S3 partitioning, Timestream, Aurora/MySQL), orchestration, inference pattern, and cost/scalability tradeoffs — and document the design before large-scale execution, since backfills across tens of thousands of assets are expensive to rerun
- Build and scale distributed data and feature pipelines over large historical sensor datasets using Spark (EMR, Glue, or SageMaker Processing), Athena, and Parquet-based data layouts, with attention to partitioning, throughput, and compute cost
- Design and run data mining and annotation workflows that connect Condition Monitoring Engineers with Data Science — including case discovery, labeling interfaces and conventions, fault-type and time-window labeling, label QA, and turning expert feedback into reusable supervised training data
- Build and maintain benchmark, training, validation, and regression datasets using real machine cases; work with Condition Monitoring Engineers and other domain experts to establish reliable ground truth and measurable model acceptance criteria
- Design experiments and compare new algorithms against existing production methods, investigating false positives, false negatives, suppression behavior, data-quality issues, and performance across different machines, operating conditions, sampling configurations, and sensor characteristics
- Develop robust, modular, reusable, and maintainable ML software in Python, including production-quality model code, shared libraries, configuration, unit and regression tests, code reviews, refactoring, debugging, and version-controlled development workflows
- Use modern AI-assisted and agentic coding approaches to accelerate data preparation, feature engineering, model prototyping, testing, debugging, pipeline development, and documentation; develop or integrate LLM- and agent-based decision-support capabilities where they provide measurable value
- Collaborate with the research and engineering team to constantly refine and improve model architectures, algorithms, and signal-processing approaches, ensuring high accuracy, explainability, scalability, and maintainability
- Work closely with MLOps and Data Engineering teams to ensure smooth deployment of models, feature pipelines, data contracts, and other ML solutions in production environments, including CI/CD for ML, model versioning and registry, IaC (CloudFormation/CDK/Terraform), containerization (Docker/ECR), and production monitoring and alerting via CloudWatch, and participate in production validation and troubleshooting
- Stay updated with the latest advancements in Machine Learning, Deep Learning, Signal Processing, agentic AI, and industrial condition monitoring, ensuring our solutions remain at the forefront of the industry
- Present findings, model performance, strategies, tradeoffs, and solutions to other teams and stakeholders in a clear and concise manner, ensuring that insights drive actionable outcomes
Our take
Across the manufacturing industry, lost production time equates to over $1 trillion of lost revenue each year. Much of this is due to foreseeable errors in machinery, many of which could have been fixed before they broke. AssetWatch, previously known as Nikola Labs, works with manufacturing companies to reduce unplanned downtime in their machinery and technology.
Focused on discovering machinery faults before it is too late, the company's wireless vibration sensors connect to a team of certified vibration experts who leverage machine learning to analyse the data. Hence, its prescriptive and predictive maintenance alerts signal potential faults before failure occurs, thus reducing potential downtime from these machinery faults.
With a wide range of solutions spanning software to IoT, AssetWatch appeals to everyone from factory managers to leadership teams, giving everyone a comprehensive overview of manufacturing health. The company has enjoyed significant funding of over $170 million so far, and has enjoyed substantial growth since its founding in 2014. It plans to continue expanding and scaling up the business.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Top investors
Company
Funding (last 2 of 9 rounds)
Apr 2025
$75m
SERIES C
May 2024
$38m
SERIES B
Total funding: $150.7m
Company benefits
- Equity options
- Flexible work schedule
- Full benefits and 401K + 401K Match
- Opportunity to make a real impact every day
- Unlimited PTO
Company HQ
Collegeview Park, Westerville, OH
Leadership
Will Zell
(Co-Founder & Executive Chairman)Previously CEO of AssetWatch. Currently Founding Partner of Ohio Angel Collective, Founder of Vessel, and is on the Advisory Board for IKOVE Venture Partners.
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