Senior Software Engineer, Forecasting & Real-Time Optimization

Lyft•Seattle, WA

1d•$136,160 - $170,200•Hybrid

About The Position

The Senior Software Engineer will join Lyft’s Forecasting and Real-Time Optimization Platform (FORTOP) team in the Rideshare Experience & Marketplace organization. FORTOP provides real-time market supply and demand signals and forecasts that support automated decisions and products such as Dynamic Pricing, Real-Time Supply Management, and Fulfillment. The role leads the design and operation of production systems that turn streaming marketplace data into accurate, reliable forecasts. This includes real-time feature pipelines, model training and inference infrastructure, backtesting and evaluation tools, and the serving layer used by downstream products. The engineer works closely with scientists to take models from prototype to production and with downstream teams to establish interfaces and reliability guarantees. Forecasting work covers rider demand and driver supply as they change with traffic, holidays, and major events. The position is hybrid and requires working in the office three days per week—Mondays, Wednesdays, and Thursdays. Hybrid employees may work from anywhere for up to four weeks per year. The expected base pay range for the Seattle area is $136,160–$170,200, excluding potential equity, bonus, or benefits.

Requirements

At least 5 years of software engineering industry experience, including building and operating backend or data systems in production.
BS/MS or equivalent in Computer Engineering, Computer Science, or a related field, or relevant work experience.
Experience designing and leading delivery of large-scale distributed systems and owning their reliability through monitoring, on-call, and incident response.
Solid distributed-systems fundamentals, including fault tolerance, delivery semantics, and consistency tradeoffs.
Track record of leading ambiguous, multi-engineer projects and mentoring other engineers.
Clear written communication, including writing design documents that align stakeholders.

Nice To Haves

Experience building production machine-learning models and infrastructure, including model selection, feature engineering and pipelines, training and inference pipelines, model serving, or ML platforms.
Hands-on experience with streaming or large-scale batch processing systems such as Flink, Beam, or Spark, and distributed messaging systems such as Kafka or Kinesis.
Experience with time-series forecasting systems or familiarity with backtesting and forecast evaluation, including error metrics, bias, and calibration.
Experience with online experimentation or real-time decision systems such as pricing, dispatch, or marketplace balancing.
Comfort using AI-assisted development tools to move faster without compromising quality.

Responsibilities

Lead multi-engineer projects from proposal and technical design through launch; establish milestones, coordinate dependencies with partner teams, and own estimates.
Design and evolve streaming feature pipelines, model training and inference workflows, and signal-serving systems, considering latency, correctness, scalability, and cost.
Lead design reviews and reduce system complexity by consolidating or retiring components.
Partner with scientists to design and productionize machine-learning models.
Build rollout tools such as shadow, canary, and rollback, along with backtesting and experimentation infrastructure.
Define and maintain interfaces, data contracts, and SLAs between forecasting signals and downstream consumers such as Dynamic Pricing and Driver Incentives.
Monitor system health and signal quality, and prepare systems for peak events.
Participate in on-call, lead post-incident investigations, and ensure follow-up actions are completed.
Write well-tested, maintainable code; conduct code reviews; and coach teammates on testing and engineering practices.
Collaborate with Product, Science, and partner engineering teams to frame problems, shape the roadmap, and balance accuracy, latency, and complexity.
Mentor engineers, participate in hiring and onboarding, and share knowledge through design documents, runbooks, and technical talks.

Benefits

Medical, dental, and vision insurance options, with additional programs available when enrolled.
Mental health benefits.
Family-building benefits.
Child care and pet benefits.
401(k) plan with company match.
12 observed holidays.
Discretionary paid time off for salaried team members; 15 days of paid time off for hourly team members.
18 weeks of paid parental leave for biological, adoptive, and foster parents.
Subsidized commuter benefits.
Monthly Lyft credits and complimentary Lyft Pink membership.