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Über
Success in the Role What performance outcomes will you work toward in the first 6‑12 months?
First 30 days
Develop a strong understanding of Evolv's sensor ecosystem, datasets, and ML pipelines.
Review dataset structure, labeling processes, and existing exploratory data analyses.
Run initial UMAP/PCA/t‑SNE analyses to map data distributions and identify anomalies.
Identify opportunities to improve data quality, labeling consistency, and dataset coverage.
First three months
Perform deep representation analysis across sensor, time‑series, and feature data.
Evaluate classical ML and deep learning models by linking model errors to data issues.
Define data quality metrics and initial dataset acceptance criteria.
Collaborate with data collection teams to guide targeted data acquisition and relabeling.
Data mining on existing field data and understanding patterns, extracting useful information and insights.
Design methods to improve data quality, converting noisy/unverified data into clean/verified data.
By the end of the first year
Own data‑centric insights that directly improve ML model performance.
Establish ongoing monitoring of data drift, blind spots, and label quality.
Provide strategic guidance for future data collection, annotation, and curation.
Develop automated tools and dashboards for data quality reporting and representation analysis.
The Work Data Understanding & Representation Analysis
Analyze high‑dimensional sensor and feature data using UMAP, t‑SNE, PCA, and related techniques.
Identify clusters, outliers, distribution gaps, and blind spots across classes and environments.
Diagnose dataset shift, domain mismatch, sparsity, and representation collapse.
Model‑Aware Data Analysis
Conduct data analysis aligned with classical ML models (XGBoost, SVR, k‑NN, tree‑based models) and deep learning models (CNNs, Transformers).
Analyze embeddings, confusion matrices, and failure cases to map model issues back to data causes.
Data Quality & Curation
Investigate imbalanced data, noisy sensor signals, and mislabeled or ambiguous samples.
Develop strategies for weakly labeled or unlabeled data using clustering or pseudo‑labeling.
Define data quality metrics, acceptance criteria, and labeling strategies.
Work with internal teams and external vendors to improve label consistency and coverage.
Translate exploratory insights into clear recommendations for data collection, relabeling, or filtering.
Drive data‑centric improvements instead of relying solely on algorithmic changes.
Work closely with internal and external data collection teams to refine data pipelines.
Communicate findings through visualizations, reports, and technical deep‑dives.
Example Problems You Will Own
Use UMAP/PCA to detect under‑represented regions of sensor feature space and guide new data collection.
Identify mislabeled samples through clustering and model‑disagreement techniques.
Analyze class imbalance impacts across ML model families and propose data‑centric mitigations.
Cluster large volumes of unlabeled sensor data to support annotation prioritization.
Leadership & Team Culture
You will join our R&D organization, reporting directly to the VP of ML and Sensors and working with cross‑disciplinary teams of highly skilled, autonomous engineers in Electromagnetics, Computer Vision, and AI.
The team culture is one based on building trust, collaboration, ongoing development through kindness, authenticity, courage, drive, and fun.
Location & Work Requirements Where is the role located? This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of our software‑enabled hardware products, this position requires a minimum of 60% (3 days) on‑site work.
Candidates must live within commutable distance to our office in Waltham, MA.
Salary Range The base salary range for this full‑time position is $129,000 – $209,000. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training.
Benefits At Evolv, we care about building safer public spaces through innovative security technology. Our benefits include:
Equity as part of your total compensation package
Medical, dental, and vision insurance
Health Savings Account (HSA)
A 401(k) plan with 2% company match
Flexible Paid Time Off (PTO) with manager approval and business needs in mind
Quarterly stipend for perks and benefits that matter most to you
Tuition reimbursement to support your ongoing learning and development
Subscription to Calm
Equal Opportunity Employer Evolv Technology is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We welcome and encourage diversity in the workplace, and all employment decisions are made without regard to race, color, religion, national, social or ethnic origin, sex (including pregnancy), age, disability, HIV status, sexual orientation, gender identity and/or expression, veteran status, or any other status protected by law in the locations where we operate. Evolv will not tolerate discrimination or harassment based on any of these characteristics.
Evolv is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation as part of the job application process, please connect with us at
careers@evolvtechnology.com . Evolv participates in E‑verify for all employees after the completion of Form I‑9.
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Sprachkenntnisse
- English
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