Job Description Summary For over forty years, HarbourVest has been home to a committed team of professionals with an entrepreneurial spirit and a desire to deliver impactful solutions to our clients and investing partners. As our global firm grows, we continue to add individuals who seek a collaborative, open-door culture that values diversity and innovative thinking.
In our collegial environment that's marked by low turnover and high energy, you'll be inspired to grow and thrive. Here, you will be encouraged to build on your strengths and acquire new skills and experiences.
We are committed to fostering an environment of inclusion that promotes mutual respect among all employees. Understanding and valuing these differences optimizes the potential of both the individual and the firm.
HarbourVest is an equal opportunity employer.
This position will be a hybrid work arrangement. You will receive 18 remote workdays per quarter to use at your discretion, subject to manager approval. For example, you may choose to work in the office 4 days per week and take one remote day weekly (typically 13 weeks per quarter), leaving 5 additional remote days to be used as needed.
Seated within our Quantitative Investment Science group, this position leads efforts to remove computational bottlenecks. It also optimizes heavy model execution across our platform. This engineer will own performance strategy, establish SLOs, and mentor the team on performance-first thinking. This ensures QIS can support more users, data, and complexity without slowing down. This role suits senior engineers who thrive on making systems blazing fast and want to develop the technical direction of a high-impact quantitative team.
The ideal candidate is someone who has: - Extensive knowledge in Python performance tuning and low-level computation
- A track record of dramatically improving system performance (10x+ improvements)
- Experience setting and enforcing performance SLOs across a team
- Strong mentorship instincts and cross-functional collaboration skills
- A passion for finance and quantitative systems
What you will do: - Profile and optimize the most expensive functions and data transformations across the platform
- Introduce incremental and vectorized computation methods with numpy, cython, and rust when suitable
- Establish and enforce performance SLOs (P95/P99 compute latency) and efficiency budgets
- Partner with the Data Optimization engineer to ensure efficient data flow between storage and computation
- Mentor team members on performance analysis, profiling techniques, and optimization patterns
- Lead technical build reviews with a performance-first lens
- Reduce model runtime from hours to seconds for core bottom-up and top-down analytics
What you bring: - High-level skill in optimizing Python efficiency (profiling, memory management, GIL considerations)
- Extensive experience in numpy, pandas optimization, and vectorized computation
- Production experience with cython, numba, or rust for performance-critical code paths
- Proficiency with profiling tools (cProfile, py-spy, memory_profiler, flame graphs)
- Understanding of database query optimization and data access patterns
- Experience establishing and tracking performance SLOs in production systems
- Strong system build skills with performance and scalability in mind
- Experience mentoring engineers and driving technical standards
- Familiarity with cloud platforms (Azure preferred) and distributed computation patterns
- Preferred: Experience with financial data, time series analysis, or quantitative modeling
Education Preferred Bachelor of Science (B.S.) or Master's in Computer Science, Mathematics, or equivalent experience
Experience 7+ years development experience, with 2+ years leading technical initiatives or mentoring engineers
#LI-Hybrid
Salary Range $160,000.00 - $240,000.00
This USD base salary range represents only one component of total compensation for this role and is provided in accordance with local requirements. This role is eligible for a discretionary annual bonus, which is determined based on individual and overall firm performance. In addition to salary and bonus, total compensation may include eligibility for long-term reward programs and a comprehensive total rewards package that may include retirement, health, insurance, paid time off, and wellness programs. Our total rewards offerings are influenced by several business factors, and eligibility for certain components will vary by position and geography. Please note the posted ranges do not apply outside the U.S. and should not be converted to other currencies as a proxy for compensation in other countries.
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