Algorithm Developer - Nav Experience: Crowd Sourced Data Insights

Algorithm Developer - Nav Experience: Crowd Sourced Data Insights

Engineering

Manufacturing

$ 40 - $ 48 / Hour

location_onWarren, Michigan

acuteContractor

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Akkodis is recruiting for an Algorithm Developer - Nav for an automotive manufacturer in the Warren, MI area. This is an onsite contract position with a pay range of $40 hour to $48 an hour depending on education, experience and location.

This project will develop and validate an integrated framework for multi-modal, crowd-sourced data collection, visual and sensor-based hazard detection, data fusion, and application-specific insight generation. The supplier shall perform software development, model development, system integration, application development, testing, validation, and documentation according to client requirements.

The work is organized into a single major task area: Crowd-Sourced Multi-Modal Data Platform

Task 1: Crowd-Sourced Multi-Modal Data Platform

• Extend preprocessing services in the R&D development environment to handle multi-modal inputs, including visual data, inertial measurements (e.g., vertical acceleration, suspension travel), wheel speed, and other relevant vehicle signals.
• Maintain and enhance map-matching, data grouping, and publishing components so that visual and non-visual sensor data are consistently associated with map references, lane-level geometry, and road segments across map sources such as DMP and OSM.
• Implement configurable pipelines for anonymization, filtering, and sampling to address privacy, data volume, and coverage considerations across regions, vehicle platforms, and operational conditions.
• Develop and train visual detection models that can operate with IDAT-collected data to identify roadway and traffic features of interest, including potholes and poor road conditions, standing water, ice or snow-covered roads, objects in the roadway, crash scenes, broken-down vehicles, approaching emergency vehicles, wrong-way drivers, and traffic signs related to speed limits and road closures.
• Design IDAT trigger configurations that efficiently capture imagery and short video segments around events of interest while minimizing bandwidth and storage, based on model confidence, heuristic triggers, and vehicle dynamics.
• Implement on-vehicle and/or near-vehicle pre-filters that select candidate events for upload (e.g., based on vertical acceleration excursions, wheel speed variations, or other physical indicators) to increase the yield and relevance of collected visual data.
• Provide robust, versioned deployment workflows for visual models and triggers, including mechanisms for remote updates, rollback, and performance monitoring across the fleet.
• Document interfaces, model inputs/outputs, and deployment requirements to support long-term maintenance and evolution of the visual detection stack.

Task 1.3: Multi-Modal Data Fusion and Insight Generation

The supplier shall:

• Develop multi-modal data fusion algorithms that combine visual information (e.g., localized detection of potholes, road distress, standing water, or signage) with physical experience data such as vertical acceleration, suspension travel, wheel speed, and other vehicle signals to produce robust, high-confidence insights.
• Implement cross-vehicle and cross-perspective fusion to aggregate observations from multiple vehicles and trips, improving detection robustness, reducing false positives, and estimating persistence and severity of conditions over time.
• Address heterogeneity across vehicle platforms, model years, and sensor suites by incorporating calibration strategies, normalization techniques, and platform-specific feature engineering.
• Incorporate mechanisms for estimating and reporting uncertainty, coverage, and sampling bias in generated insights, enabling downstream applications to reason about confidence and limitations of the data.
• Produce lane- and segment-level insight products and historical profiles suitable for use in navigation, mapping, traffic services, and hazard awareness applications, consistent with existing Connected Horizon data schemas where practical

Task 1.4: Platform APIs and Downstream Integration

The supplier shall:

• Expose new multi-modal insight products via secure, documented APIs using client standard mechanisms such as Azure API Management, ensuring consistency with existing Connected Horizon interfaces where appropriate.

• Provide data access patterns to support both near-real-time and historical use cases, including 5-minute performance summaries, time-series profiles, and aggregated hazard statistics at segment and region levels.

• Support integration with representative downstream applications, such as navigation experiences, mapping and routing services, traffic services, hazard alerts, and internal dashboards, using client defined use-case scenarios.

• Implement basic alerting and monitoring functions for data latency, pipeline health, and model performance to support operations and troubleshooting.

• Master degree in an Engineering, Computer Scinece related field

• Min 5 years experience in industry, reeasrch, project and interships, algorithm development utilizing navigation and crowdsourcing.

If interested forward resume to tina.johnsonkelley@akkodisgroup.com or click apply now.

Pay Details: $40.00 to $48.00 per hour

Benefit offerings available for our associates include medical, dental, vision, life insurance, short-term disability, additional voluntary benefits, EAP program, commuter benefits and a 401K plan. Our benefit offerings provide employees the flexibility to choose the type of coverage that meets their individual needs. In addition, our associates may be eligible for paid leave including Paid Sick Leave or any other paid leave required by Federal, State, or local law, as well as Holiday pay where applicable.

Equal Opportunity Employer/Veterans/Disabled

Military connected talent encouraged to apply

To read our Candidate Privacy Information Statement, which explains how we will use your information, please navigate to https://www.akkodis.com/en/us/candidate-privacy-policy

The Company will consider qualified applicants with arrest and conviction records in accordance with federal, state, and local laws and/or security clearance requirements, including, as applicable:

• The California Fair Chance Act
• Los Angeles City Fair Chance Ordinance
• Los Angeles County Fair Chance Ordinance for Employers
• San Francisco Fair Chance Ordinance

Massachusetts Candidates Only: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Ref: 1643605

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